Welcome to the BioXell webinar number 72. And today with us is Thomas Ler from AstraZeneca. I'm Alessandra Vilna from the Royal Institute of Technology with me is Otto Andersson from the Finnish IT Center in Finland. Thomas will speak about computational chemistry workflow with cycles and conditions using mites. So the webinar just will be recorded. And during the webinar, you are most welcome to ask question to ask question you have to use the Q&A function that is on the bottom of your zoom panel. And you might have this symbol of this symbol, depending on the operating system that you have. You can click there, type your question, and the question will be read by one of us at the end of the webinar. And but you have also the possibility to ask yourself the question. So just writing if that you do want to that we activate your microphone or if you don't want to use right now microphone. We also had something new this time. So we have an after set webinar. So after the webinar there will be in AskBioxell.eu, that is the BioXell forum, a category dedicated to this webinar where you can go on for one week to ask question to Thomas about this webinar. So please go to check there for after the webinar. Let's all about Thomas. Thomas is currently a senior scientist in the molecular AI group PedastroCernican. He's working combining physics-based approach to protein-leagant binding with generative machine learning models. He got a PhD in the Center for Misfolding Disease at the University of Cambridge, and where he was using computational methods to study kinetic and thermodynamic of disorder protein. He has also contributed to the development of open source software like Plume, and that is a software for enhance molecular dynamic simulation. And now I will stop sharing and give the world to Thomas, please. Thanks for the kind introduction and the invitation. Today I'll be talking about our efforts to build a workflow manager to handle more complex workflows containing cycles and conditions. And the motivating factor for this is really the integration of physical methods and generative machine learning models. So first I'll give you a bit of a sort of motivational introduction in terms of early stage drug discovery, how we can use generative models for it, and then how this implies a need for a more advanced workflow management system. And I will talk a bit about how you can use Maze in practice, so it looks like what is implemented and some basic capabilities. So machine learning crops up in all parts of drug discovery and the drug discovery process. So of course we have late stage development and clinical development where machine learning methods are also routine, but also of course early discovery so target identification and initial drug design. So I'm going to focus on drug design today and the departments I'm working in at AstraZeneca, the molecular AI group. And we have sort of three main focuses. First of all we're working on synthesis prediction. So given a small molecule, how can we find an ideal synthesis route for this in an automated way. And the novel molecular design so coming up with completely new molecular structures that are drug like and could be good drug candidates. And then the third one, this is sort of the sub team I'm working on is to be physics informed screening aspect, and this is very tightly integrated would be the novel molecular design efforts because if we come up with a new drug compound we want to be able to evaluate it as efficiently as possible. So the core really of the drug discovery process in the early stages the design make test analyze cycle. So generally you will a drug discovery process project starts with a drug target. So some kind of protein that is implicated in the disease and will typically have some kind of initial hit compound that is a starting point for us. And this hit compound will often be not very active or selective as not so nice toxic logical properties. And we want to get to a candidate drug, which is more potent as a good side effect profile and certainly as effective as possible. Between these in that in that process, we go through this design make test analyze cycle. And so we design come up with a design for a molecule, synthesize it, test it in some assay and then analyze the results and use that to inform the next iteration of this cycle. How can we make this faster. And so what we're working on in my group is is a lot of doing a little design as I mentioned before so coming up with new structures, synthesis prediction, and what is also done in AstraZeneca is automation to make the test and analyze steps as efficient as possible. And I'm not going to be talking about those today. So, do you know how my design is, is sort of a fairly new field and it's really been made possible by innovations and machine learning, especially. And so I think a lot of you are now familiar with large language models and actually the main software that that was developed in house and is, and is available for this purpose called reinvent is actually based on ideas in language models. So what we can do is we can take a small molecule, like this example here, and we can use the smiles representation which is basically just a string representation of this molecule. And smiles codes have a really can be seen as a language in some sense so they have a specific syntax and grammar to it. So we can use the, the power of, of language models and apply them to this task of generating small molecules. And this is done in our case with recurrent neural networks which are a bit of an older method that they work really well in this case actually. So what it is we can sample from this recurrent neural network, after it's been trained on a suitable set of traffic molecules for instance, and eventually build up our molecules so in this case where we get to this, this probability vector and we sample from this probability vector. And this case we're sampling an oxygen atom. And because we sample an oxygen atom we have a certain other probability distribution the second iteration will be a double bond and then eventually we can build up our small molecule by sampling from the output of this network and we get something that is a drug like molecule. And about something like what you see on the right side. This tool is called reinvent and it's really be the core of our design efforts. And as I just mentioned the idea is we propose some new chemical structures, but ideally what we want to do is we want to get some kind of idea of how good those small molecules are at a particular tasks so for example binding to a particular drug target. So what we want is some kind of scoring function that evaluates them, and then feeds those scores idea back into the generative model so that at the next iteration the generative model can produce a better set of compounds. This process called reinforcement learning. And you might have heard of this in in many other sort of high profile machine learning projects. And so the advantage of this is that instead of screening through existing libraries which might have a few billion compounds at most, we can actually screen a much, much larger section of chemical space. And the number that gets thrown around the chemical space was 10 to the 60 compounds. And of course a lot of that depends on how the model is trained but we can definitely generate many compounds that have never been seen before. So in theory this gives us a lot of flexibility in our drug discovery process. And you can see this reinforcement learning at play if you use docking as a scoring function. And in this case you can see the docking score on the y-axis and the epoch on the x-axis. And you can see that the model logotype learns to create compounds that have a more favorable docking score. So the idea now, of course, is that we want to go a step further with this scoring process. So docking is quite nice but it's also not necessarily the most accurate way of determining the binding energy of a small molecule to a protein target. And a far more accurate approach but also very computation expensive one is a relative binding free energy calculations. And these have been used by us quite extensively and they generally correlate fairly well with experimental binding free energy. So they are a great tool to make well informed decisions on which molecules to prioritize for synthesis, for example. But of course we would like to have this kind of scoring in a loop with the generative model I've just mentioned. The problem is that RFE calculations are quite slow and they can take several hours for a single compound or the difference between two compounds. Whereas docking can be done in seconds to minutes. So there's a nice way of sort of making this process a bit more efficient and that is active learning. So the idea behind active learning is that we can take a subset of the compounds we want to score. So say we have some kind of large library of 20,000 compounds we can select just a few hundred or so. We can do an RFE calculation on just this subset. And then we can take those scores that we got and train a machine learning model to predict the scores for all the other compounds that we didn't sample. So if we select our subsets wisely and the machine learning model is in this case it's a domain specific machine learning model. So it doesn't have to be transferable so it can be quite effective. And we can actually significantly improve this process. And we can get away with doing a few hundred free energy perturbation simulations, for example. And thus saving hundreds of thousands of hours potentially of compute time and making this whole process a lot more efficient. So this would be some motivation for having some strong way of combining all these models together. We want some efficient way of doing this. And so this is where a workflow manager comes in and this is where we're going to a more to the engineering side of this whole process. So why would you want to use a workflow manager? Well, first of all, you can really make a lot of gains in terms of reproducibility of your of your scientific workflows. If it's a very standardist way of running them, then that makes things easier not just for yourself but also others who want to replicate your workflow. Then another aspect is configuration. You can separate the configuration of your software with that of your workflow or your actual scientific workflow makes it very clear which parameters are for your workflow and which ones are just there because you're running on a particular machine somewhere. And of course, you can gain a lot in terms of modularization because of many components you're using in workflows will be reused in other parts and you don't have to rewrite them again or share code in some other way. And then it can be a lot easier to automate these workflows because the interfaces can be kept very consistent. And also there's the concept of abstracting away very complex workflows in so that you can really focus on the essence of your workflow and don't have to worry about details for some particular step that have been already fleshed out before. And then finally there's some, there's some hope that you can actually parallelize quite significant parts of your workflow and thus make your whole setup more your pipeline a bit more efficient. And so you can see here is sort of an example workflow that is very typical I think in molecular simulation. So if you want to simulate a protein with a ligand, you would have some step of preparing the ligands and preparing the protein those can be run in parallel so there's some efficiency saving here.  So this is a very typical setup and these steps can sort of be seen as we can abstract in my way to boxes that look like this basically. So this is very typical news case you might be familiar with but it's actually not entirely what we want to do I think in many cases, because we might do our analysis at the end and then find this is not actually converged we need to run a bit longer. And in this case you actually immediately have a cycle in there and that's where existing workflow managers break down because basically not none of them can actually run a circular workflow like this. And so this these graph structures you saw, they can be classified as directed a cycle graphs or directed cycle graphs and directed a cycle graph we have a very linear workflow. So, any node just depends on the woods that came before it and then that came after it, whereas in the directed cyclic graph can see that node be actually depends on note a but also downstream node. D. So, there's a cycling here which cannot be easily modeled with the ways that workflow managers running directed a cycle graphs. So there are certain algorithms that are very optimized for directed a cyclic graphs that cannot be applied to directed cyclic graphs, because they depend on this ordering. And so there's quite a lot of software that can run directed a cyclic graphs, some larger piece of software airflow and Luigi. And on the other hand, directed cyclic graphs, there's not very much I found actually that can do this that we could base our work lives on. And the only big one I found is is which is written in Scotland has a bit of a different different use case. So what do we want out of our workflow manager. So, I think I mentioned these, these things before so but it's what we wanted, mostly is a reproducible system and we wanted this to be written in Python because all our other software is is written in Python and it's an excellent language for for scientific computing. And we wanted these workflow definitions to be very simple and portable. So, can easily run them on other systems and that sort of goes into the next point of configuration we wanted to keep the configuration for the system you're running on completely separate from that of a workflow. So as long as your system is configured correctly, in terms of what software can use and what is available in terms of libraries and so on. So we can just take a workflow and run it on on that system without any changes to the workflow. And we wanted some way of modularizing our workflows of course so we want to be able to reuse workflow nodes, and we can do that by giving very well defined inputs and outputs, so that they can just be put together like little pieces. And of course we wanted some ways of automating this so good interfaces to excellent software for example we can use this in Jupyter notebooks for example. And a very big point here is also the some way of abstracting our workflows into what I'm calling subgraph so the idea is you can group many nodes together to form a subgraph which in turn acts just like a normal node in a workflow would means you can build up workflows hierarchically by combining very small steps together and then abstracting them into one big step and then use those bigger steps in the larger workflow and you can do this arbitrarily deeply. And this makes I think it I think makes it easier to reason around very complex workflows that inevitably crop up in computational chemistry. And finally, it would be nice to have parallelization by default. So, being able to run all these steps in parallel and let them communicate with each other. And this brings me to what I use as a basis for this whole system and that is the concept of flow based programming which is something that was initially discovered I think in the or invented in the in the 80s but then never really caught on. And this is a very elegant way of solving this problem because the idea here is that each node in your workflow graph is just running a single system process. So all nodes in your system are running at the same time, but they all communicate with each other through specific channels based on how you connected them. One, one node can send data through the output port and some other node will receive this data and can do some processing with it. And so every node just waits for data to be received or does some processing and then sends it on and then can do something else. And the advantage of this is that you can now execute any kind of graph structure can be have arbitrary cycles or some kind of conditional execution, but also all nodes are running at the same time which means everything is parallel by default. And you can buy just very, I mean very strict about what the nodes are sending and receiving, you can completely avoid any problems like race conditions that you might normally have if you're trying to run software in parallel. So this allows us to solve quite a few problems and gives us some interesting patterns that we can implement that wouldn't have been possible otherwise. So the first one I can I can show you is on the left side and that's batch processing. So we have some kind of long list of data that we want to process through to compute nodes compute a and compute B. And the way we can do this is we can chunk our data into into batches and send our first batch to compute a which will process it send it to compute B process it and send it onwards. But say we have compute node a is is some kind of process that only runs on the CPU and compute B is something that only runs on the GPU. And sometimes we can be far more efficient with our data by doing this because while compute B is running the second batch on the GPU compute a is is running by the computer B is running the first batch of the GPU compute a is running the second batch on the CPU to prepare for compute B. Far more efficient use of resources by doing this. Similarly, you might have software about your two GPUs in your system and your software can only use one GPU at a time. What you can do is you can parallelize your system in such a way that you have to compute nodes doing the same thing just running on separate GPUs for example. You can split your data and send some of it to first node in the other half of it to the second node and let them do their computations and then merger results. And finally, the problem I mentioned earlier with with having some kind of, for example, convergence check is essentially just iteration so we have some kind of compute process happening. And we want to check if we're done and if we're done we just go out of the loop that's fine and if we're not done we send it back to the compute node and we can murder results data. So this is sort of an example of what you could do if you had a simulation running and you wanted to do a convergence check. So this brings me back to active learning. So the, the idea here is, as mentioned before, we have some kind of library or generator in our case. So some way of generating small molecule candidates. And we send them to, we sample from this generator. And before we actually run this whole thing, we have some kind of, I'm assuming we have some kind of model that is trained to predict scores, even if it's not in a very efficient way. But we have, we send it to send some samples say 1000 structures from reinvent, we send them to this model that predict scores. And then based on these scores, we can select a subset, say 100, and those hundred are sent to the so called Oracle, which is the expensive function that we actually want to model. So the Oracle can do this calculation on a subset of 100 compounds. And the scores that it calculated of the free energy of binding, for example, that they calculated and sent back to the, to the model, and the model can be retrained with this data. And at the same time, of course, we merge these results, what we got from the Oracle, say the hundred with the other 900 that we got from the, from a cheap model and feed these scores back to, in this case, reinvent. So the reinforcement learning loop. And this way we can hopefully get a better set of small molecules the next iteration. And at the same time, our model here is going to get a lot better over time, because we're doing it more and more training data from the expensive Oracle function. So the general idea behind active learning. And here's sort of an example of how this would look like in in maze. So we have some kind of generator that generates a list of smiles. And these are sent to the, to this model, the surrogate model that predict scores for all of them. So we have some kind of set of scores. And this goes to this acquisition function. And this acquisition function decides which molecules to send to the Oracle function that is very expensive and which ones to just send back to the generator eventually. So, in this case, we have these two, these two compounds selected, or this one selected for an article and this one selected to just send on. The Oracle does its calculation, sends on the result to a node that just copies the results. And this, this result will be sent first to the, on one hand will be sent to the surrogate training nodes. So we retrain our model on this new score that has been calculated. And the retrade model can go back and been used in the next iteration for the next prediction cycle. In the same time, everything is merged back together and sent back to the generator. So, this was sort of the, the general motivation for, for designing this. Specifically, using it in active learning workflows which incorporate a lot of different types of software we have generative models we have some kind of scoring models we need a lot of sort of what I'm calling plumbing steps in between. And it allows us to be very, very flexible in terms of what kind of workflows we put in so we could think about putting in an additional model in the active learning workflow, for example, or additional scoring steps, or deciding using some other methods and then using those scores to decide which ones to send to an RBE calculation and so on. So there are lots of different combinations we could do here to optimize these, these workflows and make them fit for a very particular purpose. And so there's a bit of software that we've already implemented in terms of workflow steps that can be used right now. And so one of them is, is of course reinvent. So, it's more multiple generative model.  We also have a shape matching this is rocks. So this is an alternative to docking in some sense and it can be more accurate in some, in some cases. So we have a lot of sort of utility functions so a lot of loading molecules writing molecules. We have an RMSD filtering step so we can, for example, after docking filter compounds according to how close they are to the to some kind of reference we have embedding steps so this allows us to embed go from smiles to to an actual embedded small molecule isomer with 3d coordinates and some kind of state and things like preparing the docking grids for software like you can do online. The other side is more expensive stuff so we have a fairly simple interface to be a relative funding for energy methods implemented in PMX through a previous workflow manager called I close that we had that is only for linear workflows. So we're working on implementing gromax is a big priority here. And we also have some people working on more semi empirical and a new sheet of things xdb pressed and Gaussian. And among all these other things are lots of utility functions so filtering data and some some way. Additionally to different branches. What's a file IO and things like this. So my co worker Lily is is working on implementing gromax in this framework. And this is where I mentioned sub graphs earlier where this can really come into play because we can implement the individual gromax commands but then of course, normally you would want to. You can do a lot of stuff that you're grouping things together and you're running the same set of commands every time you do some kind of action. So we can do exactly this and group lots of notes together and handle the copying and state manipulation this way, and then have very simple workflows at the end that allows to give us a very course of what is going on, and then make it a lot easier to reason about things. And then the other hand we have to my co workers, we could ask them to show for working on predictive synthesis models. And the idea here is that we have some kind of reaction metadata we've already, we have from some other resource, and we want to evaluate certain properties of this reaction. And so what we can do with with maze as well as we can start this circular work flow. And that is generating conformers doing some semi empirical simulations and possibly DFT simulations to evaluate a certain reaction, and then eventually using this data to train an in silico model to actually predict the impassability of a certain compound, for example. So now I will just switch over and show you some actual code and how you can use it. This works. So, okay, I want you to just quickly go through sort of a classic example which is a simple docking workflow. And the idea here is, so this is a Jupyter notebook, and we're going to run a simple linear docking workflow we're going to take a few compounds, embed them. So it smiles and embed them to make them give them actual 3D structure, and then dock them to a protein and get the results back. So let's just show you a real basics of maze and how it looks like using it. So there are certain things you need to import. No pointing into too much detail here but there are certain, certain notes that are available here that we are going to use specifically autotour QPU and gypsum VL, which is a method to embed small molecules. So to go from smiles to different isomers and different conformers. And something I should maybe mentioned before, continue much further so earlier already mentioned so maze is written in Python but it's also, it's designed to be specific to the packages and in some sense so it's, we have maze the core package which is completely domain agnostic. And we have the so called maze contrib package which contains stuff specific to computational chemistry and on the domain specific code. So maze is not necessarily only there for computer chemistry you could use it for any kind of workflow management. I mentioned earlier that we keep our configuration completely separate just for this example and because we're running a notebook it's a bit easier to just show the configuration right here. So this is what it would look like you would specify okay auto.tpu needs specific modules so this is what you might be familiar with from some HPC systems would want to do a module load CUDA module load GCC. So this is basically this and we tell it's where to find the software, and this comes in handy when you have software that doesn't come with a fixed name so gromites is another example. You can sort with or without MPI for example have a different name. So this is to avoid any hard coding of things like this. Now for for talking we need to have a grid. So this is already there. And we're going to start by defining our workflow which is going to be done by instantiating a workflow object. And we're going to tell it where the configuration is in this case we're just going to use this example configuration which is what you just saw above. In a real case you would have one file for your for your whole system, and you can put it in one spot and it was find it there. And you would never have to change it depending on the workflow. Then we can start adding nodes to our workflow. And Maze makes use of the typing features in Python, which are fairly new addition to know about them that basically allows you to give type annotations to to any object in Python. And in this case makes me so this to determine what kind of data is being sent around from node to node. And I thought in my experience it can really help with finding errors in GrassWay is sending maybe the wrong kind of data from one node to another. And it will catch this before before the workflow is even run. So in this case we're we have a node called load data which can load any kind of data. But gypsum as a node needs a list of strings it's input because it's getting smiles codes. So we need to tell the data that it's going to load the list of strings. So this is the data type for this, for this node. So we just add these notes, and the same applies for the return node which is a way of returning the data back to the current Python interpreter having been run through the graph. So in this case we can, we can set up the data we want to load in this case we're going to dock these two small strings. And we're going to tell gypsum that handling the isomers and some conformers how many variants to generate the maximum. And we're going to say tell auto.tpu where the grid is. We can connect things together so at this point we haven't actually connected anything to it we've just said there are these nodes in the graph. And now we're actually saying okay this is connected to this and so on. And this will be a bit clearer if you look at the visualization of the workflow we've just made. So we have some data loading, some node loading data here. And it will show the data type. So we're loading a list of strings sending them to gypsum. Gypsum is generating a list of this isomer collection which is essentially just a thin wrapper around our decit molecules. These are sent to auto.tpu. Auto.tpu has two outputs there in some sense they're a bit redundant. But because there is always a bit of an overhead in parsing, parsing conformers from the results from docking and we don't always need them. There's also a score on the output so this can be a non-py array. That just has the scores for a certain docked compound and also the list of docked compounds containing conformers. And then all we need to do is run float and execute and it will run and give some information. And we can get the results of this from the last node so this was the last node we defined this return node and we can just get the information out of it. This contains the actual molecules that have been embedded and docked. So we have a score, in the first case a molecule failed to dock and in this case it's worked. So we have a score which is a terrible score for docking. And then we can actually visualize this and of course this looks a little bit weird because it's a docked conformer. And then finally I thought I'd show you what you would do if you wanted to add a custom node to this. And what you need is in this case we're just going to need an input and we're going to need the special node class. And what we do when we define a node so it looks something like this. We define a new class and we give our node a name and we subclass from the node class in maze. And we declare an input and this is where we say what this node gets as an input. What kind of data in this case it's an umpire array. And we have the input for it. And then we always define a simple run function which just determines the logic that is in the node. And in this case we're just receiving from this input. And this call will just block until it gets some kind of data. So once it gets data this will immediately run and we'll get scores and then the node can just do whatever it needs to do with scores. And in this case we're just going to log whatever scores we got to the console. So again we can build up our work flow as before and it'll be basically the same except that we're here adding this node that we just defined. Instead of the void node that was there earlier which just deviated to whatever it got as input. And again we can run it and we'll see. We just got this logging message telling us what the docking scores were. And I think I might have just a few more minutes just to want to be other examples I mentioned earlier in terms of using Maze to do some kind of batch processing or parallelization. And so in this case we can do this with docking. And docking is sort of a good example for this because in this case we're running with AutocomGPU which this may imply is the one that's only in the GPU. But we also need to embed this one first. Which means that gym practices can actually be in my experience just about as though it's docking itself. So it would be good to parallelize these steps. And this is something that's done in the CPU. So what this means is that we can build up our work flow and I'll just show you the graph because it makes it a little bit clearer. So again we load our data in and we batch it. So we send out chunks of it at the time and we run these two nodes in the middle in a loop. So they continuously receive data and continuously process it. And then whatever we got out of this whole thing, send it to a node called Combine that can merge all these chunks back together again. And so with these, with this batch on Combine node the interface is exactly the same. So we could combine this into a sub graph and it will behave just the same as just these two. And this will make it possible for AutocomGPU to be docking while Gypsum is embedding the next batch of small modules. And this will run just the same. So you've finished Thomas? Not quite. Okay, so you have the last switch from now. Yes. So just to sort of summarize this. So I think it might be interesting for anyone who has to work on a bit odd software or software with conflicting interfaces or conflicting environments. And something I just want to quickly mention is that actually Maze allows you to run because each node is running a separate process in Python. It actually allows you to have a completely different Python environments for each process for each node. So in some cases you might find you have, I've noticed this especially with machine learning there's some package that absolutely needs some version of PyTorch and some other packages completely incompatible with that version. You can use them together in the same workflow but having separate environments for them. So that's another case where this can be useful. And of course, any kind of with parallelization requirements and quite a few situations where not completely aware of the, of some kind of opportunity for parallelization but it crops up naturally in the graph. So all the overcode I just showed is it's all open source and the permissive license and it's all in our monocled AI GitHub, and I think there'll be links in the chat as well. And if you have any questions on this you can get in touch or give us the forum. So yeah, thank you very much for listening and happy to take any questions. Thank you very much Thomas was great presentation. So we have a couple of questions. The first question is from Valentin Valentin if you're still there we will unmute you. If you can talk you can just speak and ask your question. Hello, is it working? Sorry. Yeah, it's working. Please go ahead. I was just wondering how can you make sure like that you get a small fraction of a family of one like is for a specific project to have an importance in the in the machine learning model. What do we narrow it down? Yes, like narrowing down really specific ligands for the protein rather than than the like, how can you avoid like the bulk ligands that you don't really need or use? Yeah, that's a good point. I mean the model through the reinforcement learning process model learns to generate molecules better actually suitable for the target protein if your scoring function is good. So you can, there are different ways of doing it right here. You could, you could sample very small batch sizes and then just wait a longer time. We sample huge batches and that we scoring functions sorted out that way. So it's super clear what what is actually better. And then in terms of narrowing it down even further so I just mentioned very specific scoring functions right like talking but of course in practice you would you would use a lot of other stuff as well so you can start using things like like the Pinske's rule of five for example there is a very, very simple one or have some kind of limit on the molecular weight or toxicological models, other QSR models. So there's a lot of options there and if you start combining them then you're really shrinking the space available quite dramatically. Okay, that was great. If I can make another question. You do generate molecules out of nothing, right? You don't get already made database. Yeah, that's correct. Yeah. Okay, thank you. Thank you. So now the next question is from Cesar. Okay, Cesar. I will allow you to speak if you want. Would you speak? Hello. Hi. Yes. So I got interested in how you implemented the FEP on this all workflow. I mean, if you use relative binding free energy, you cannot access to all the molecules because it's relative. It's for the congenetic series. So how do you access to run all of them and compare between all these medium compounds? I don't know. Yeah, that's a good point. So at the moment, what we're doing is we're generating based on a sort of a constrained inner scaffold. But that the actual space is sort of fairly limited. And it means we have a good reference. And so the steps we take in our BFE are going to be relatively small. So that is what we're currently doing. But we're definitely also considering at some point absolute binding free energies to make sort of scaffold holding possible. All right. Yep. Thank you. This is available, right? Okay. Thank you. Thank you. So now we have another question of Erica. Now I will unmute you. So just give me a moment. I guess, and Rika is not anymore online. I don't found her. Okay, I will read the question. Hello, very interesting. Are you treating each frame of your simulation as a single data point? Or is the entire trajectory per replica? I'm not quite sure what the context was exactly of this, but generally we don't treat, I mean, for RBFE, we treat the whole simulation as a data point. So we take the case of RBFE, we might do several replicas of the same of the same system and then treat that whole thing as a as one data point. Okay. Thank you. And I will see. Just check the next question. If this still is from Bruna, we can ask from Bruna. So I will allow you to talk if you can talk. Sure. Thank you. So many things for the presentation. I was wondering if you want to implement OpenMM in this application. Yeah, that's sort of fit me on my on my list. Yes, would like to get this. Oh, great. And then we have a question I was trying to from BL. I will allow you to talk. Would you talk? Can you hear me? Yeah, please go ahead. Yeah, thank you. Thank you for such a nice presentation. So I was wondering if this maze can be used along with a job schedule or something like Celeron or PBS Pro. In the environment, a lot of colleagues are running a job in the same dedicated like in a shared compute environment. Yes. Yeah, that was a big, a big thing we wanted to definitely have in there. So I didn't show just now that there's sort of built in commands for running built in functions for running any command and you can dynamically decide if you want to run it on on Slurm, for example, or locally. Of course, you could run the whole workflow, but that's not really doesn't really have much to do with with Maze itself. It should be the, I mean, it's not the part of Maze, but it can be done in a certain way. Okay, thank you. We have a last question that I will read because I don't see the person online. What is the least software requirements to run the program in a normal computer, or do I need super computer to run? It will run on any computer. I mean, I developed it mostly on my workstation, which has a course in the GPU, but it really depends on the workloads you're using, but I don't think there's any real limitation here. So I thank you a lot. I just want if you could stop sharing, I will start again sharing. So I want to have just a final announcement. Just give me the option to go to my PowerPoint that is here. So I want just to make an announcement so we will let the next webinar by Excel webinar will be the 10th of October at three Central European time, and it will be on the competency app by Martin Lorenzo Minaris that and in particular is a link to the competence app that browse competence career profile and training research linked to the computational biomolecular research, so by Excel. I thank you everybody for the tennis, and I hope to see you next time. Thank you. Bye.


Season one, episode five. So I'm passing math today, which is good because I'm going to talk with a techie and she checked my math. I'm so glad to be back with you and really excited about today's guest. I'm also excited that this week I got my COVID or last week Thursday, I got my COVID vaccine dose two of the Pfizer and I know many of us across the country are kind of excited about making progress against the pandemic. My only side effect was I felt a little crummy and felt like taking a nap and 13 hours later I was fine. So I got the best nap in my life probably. So let's hope that society progresses down the vaccine trail and we overcome this really tragic situation. I do want to thank before I start think tech Hawaii for allowing me to present pigments the power of imagination. They're a 5013 C corporation which I think means they need your donations. I know they do. So help out think tank they've got a tremendous diversity of content, and they rely on you the viewers to support them. So please do. I have a great guest today I'm really excited about having Lieutenant General Susan Helms on an old friend from my time at Air Force Space Command and Susan how are you bring bring it. How are you doing. I'm doing great big it's great to see you again. Likewise. So you were in the first Air Force Academy class with women. I couldn't even get into the Air Force Academy in fact I think they might still stop me at the gate. You were a flight test engineer, you were the first military woman in space.  Holy cow and that doesn't even cover your most difficult assignment the Air Force. I think I know what you're going to say. You had to teach me about space you and Carl Marty France, another great space command officer, got stuck with teaching the Neanderthal fighter pilot about space and how stuff works in space. What do you remember about that just because it had to be your most difficult job. I remember how quick of a student you were. I, I remember really thinking when I explained space to a lot of people it does sometimes take a while for folks to catch on if they're not familiar with the material but you caught on immediately and not only did you catch on to the physics that were going on, but you also caught on to the implications of what military space would really bring to the fight. That's what I remember about that half hour I think I spent in your office with a chalkboard and a piece of chalk and a few hand gestures and things like that. Right. We don't we don't do this. Exactly. While you and Marty did a great job and I was, if I was quick it was because I was passionate about it because I never flew a combat mission of the f 15 and f 16 combat missions I flew that wasn't very much enabled by space capabilities. So, so I was passionate about it. I don't know if I told you my shuttle launch story at the time I shared this right before we came on the air but, but my earliest exposure to space was when I had a chance to see a shuttle launch and didn't prepare for this. I think you might have been on that mission in discovery mission in 1994. But as a group commander I was down at the shuttle landing strip. Right. Patrick Air Force Base. And I went there to meet with one of my squadron commanders that a helicopter squadron that supported shuttle landings and there was a delay. Was there a delay on every launch you ever flew on. You know that one was really interesting because we didn't think we were going to launch that day either. But nonetheless they thought well just in clay just in case the weather breaks let's load you all into the spacecraft. So we woke up that day thinking there's no way I mean the weather was terrible that day. And our window for launch was about two and a half hours. So at the two hour and 10 minute point so we had 20 more minutes left to go. Mission control and launch control came on and said hey we got a break in the weather you're going now. So, literally had. We had to mentally switch our heads from today's not a day we're going to launch to we're launching now and it was really about that quick. That went on time. I'm sorry. Go ahead. Really all of your other missions went on time. I was not that quick Susan because Bob Hall that Holloway my 41st rescue squadron commander and I were sitting in the blockhouse there at the landing strip and remember that. And just having a great kind of father son I'm his boss conversation. He's a fine squatter commander. And we heard this noise and what. What do you suppose that is well that is the motor starting on the shuttle. And we run outside like a couple of idiots, which we were. And I swear Susan, I saw this contrail that went up to a cloud and disappeared behind a cloud. And because of the geometry. I never saw the shuttle. Who else can do that. Well, we were almost as surprised as you were. But you got to participate I didn't. So, that's true. What is, you know, with all of your experience and we'll get to the longest spacewalk in history which you owner of that accolade of fuel. But if you were to tell the general public, our viewers, one thing about being in space operating in space being an astronaut that you'd like them to know what would it be. I would like people to know how peaceful life is up there on the space station. You know, when I was in space in 2001 that was my last mission that was a six month duration flight on the International Space Station.  Because we were away from almost all email, all phone mail, all that noise of life we didn't have television we didn't have any kind of, you know, social media of course back then. And, and it was peaceful, and it was so peaceful that when I came back from that six month mission. And the difficult transition to make after being in space for six months was getting used to all the noise of life again, which, which I did not expect, and no one told me about that in advance but it was a very difficult transition psychologically to come back from a long duration space flight and be around people again. And that was through a three people total me and a Russian and another American and, and all three of us were. If that was a tough transition actually more tough and physical transition to come back. Did you speak Russian or English. Oh, we spoke Brucelish. Jim and I Jim boss my space walking crewmate. He and I had to learn how to speak Russian because half of the space station at that time is Russian provided. Right. And Yuri, Usachoff our crew commander had to learn how to speak English. So between the three of us over four years of training and then six months on the space station we just kind of came up with our own little crew language of Brucelish. And it worked extremely well we understood each other pretty much perfectly, even people around us couldn't understand us. So that six month mission you train for four, four years for. Yeah, I think that's, I think that was the number. It's I started in 97. And we launched in on March 8 of 2001, just had our 20th anniversary. Wow, a couple weeks ago. So our peaceful is our takeaway about space is it's peaceful. Yes. And beautiful. Like, seeing that in, but only in pictures and, you know, maybe I've flown fairly high but not like space. So how does somebody like you wind up in space what was your motivation and of course I'm answering asking questions I know the answer to, because I know your dad if we could see the picture of your dad was the key part of your motivation and let me talk about technology that's an IBM selector typewriter, I think a beautiful bit of technology at the time. For those of us had to type term papers and stuff but so your dad I know was your motivation tell me about about that and how he inspired you because we're about entertaining and inspiring here on thickness. So, you know, my, my parents, of course, were really awesome. My dad was a career pilot in the Air Force, and he had flown in several different kinds of airplanes. And I think that as I looked at what he did in the Air Force there was so much about what his life was like and what his career was like that was really inspiring to me, you know serving his country. Of course, spending a year in Vietnam. In addition to that it was a really, it was really compelling to me to travel around it was compelling to me to move often and see different places and get a chance to travel of course I didn't realize at the time. How far I would travel, but further than me. So the Air Force offered lots and lots of opportunities to do some really amazing things while you're serving your country. And, and then on top of that, in the 70s, you know, for me to join the Air Force I would get equal pay for equal work. That was another motivator back in the 70s, because there weren't a lot of places where you could do that. And the young lady woman, did that give you a bit of an edge did you know was there a little bit of, you know was that something you really wanted equal pay for equal work. I think, I think actually my dad and my mom both were always very supportive. So, for them. Yeah they, my mom and dad both were of the mind that there was nothing I couldn't do if I didn't put my mind to it. So, if I would just put my mind to something, I could make it happen, and they both believe that. And I grew up in a family of daughters I, I don't know if I find that a compelling fact or not but I was the oldest of four daughters. And my parents just absolutely made sure that if there was something I was interested in doing, they would ensure that I would understand that was achievable. And I loved math and science as a kid, and I decided I wanted to get into something that was math and science related, which was where the idea of becoming an engineer came from, since it's also a creative career. Yeah, well, you're also an artist and pianist, I think, and a musician.  I got that right. If you like math and science and you also want to be creative. The engineering career field is a great way to go because it's inherent nature design is part of the creative process with math and science. I am wearing my university of Hawaii shirt today in honor of Alejo.  But when I shared that with him, he's an artist and musician. He said, yeah, it's a logical path to creativity. I really like that description. I can't do it. I hate math and science. I have no musical talent. I just wanted to fly. But as it turns out, you kind of just wanted to fly to and we'll get back to that right after we talk about our coming episodes. Okay. That's good. So, in our next episode, Eric can be there we go. We're going to have Andre Maria Jacquet on who are tremendous friends I've mentioned them for several weeks now, lead writers for the first several years of mad men, and they take their figments and sell them. And I've worked with them and have gotten to know, folks, you're going to love this that you're going to love the inside Hollywood look at it. And you're going to really enjoy meeting two wonderful friends of mine.  Now it doesn't always have that happy and ending. But Kim and Bruce are going to share their stories. And I know you'll be inspired by that. And by the way, Kim or husband Ross who I try to take money from on the golf course every week. Susan all taught at the Air Force Academy together and recently met again on vacation, right, Susan. That's right. I just got back from Texas where I met up with them for a spring break. It wasn't traditional spring break it was in a bubble but I had a great time and I think Ross said he was going to take your money. So whatever, whatever. It is a small world and you've seen it from a perspective where it's a real small world. But you wanted to fly. Right. I mean that's what you really wanted to do and I know that feeling. So, tell me about how you felt about flying and getting in the air. I, when I was a test engineer, it, it became obvious that flying high and fast was is one of those things where you wake up every day going I can't believe I'm getting paid for this. I'm doing engineering while I'm in the air on the jet, but to get to fly while I'm doing what I love all at once was really like living in Disneyland actually. So when the astronaut opportunity came along. I would say well, you know what is it about being an astronaut that really appeals to you and I said well I get to fly higher and faster than I've ever flown before and I get to see a beautiful view at the same time. So I must have something about my dad inside of me, because he loved to fly. And, and I'm the only daughter that has taken this path. Yes, just flying high and fasting. And of course, you know it's kind of hard to beat the space shuttle for, you know, a rocket ride. But she don't just wind up you don't just say you know I want to get on the space shuttle and you've got to pay some dues, and you put your living where your mouth was and said after test pilot school. And so Susan viewers went to test pilot schools of flight test engineer, and then chose to go to cold Lake Canada, and from Honolulu. That sounds crazy we have a picture that I shiver when that's you on a Canadian CF 18 Hornet, yet ready. But that's cold man. It's probably June right, June or July at cold Lake. Oh, probably. Yeah, but probably is, but man the flying was great up there. And you flew in CF 18s and their version the t 33 I think what else f fives, and then they had Kyle was and twin and then they had a Canadian unique airplane called the challenger that I never saw fly because it was having issues. Having challenges, I guess. Yeah. But yeah, it was the f 18 and f five were obviously two of my top favorites, but they let us fly and everything which wasn't necessarily true at Edwards are eglings had to pick a weapon system. Right, right. As you, as you know I've got a son of law who's a Air Force test pilot, you do have to focus in your job. You went to cold lake. Right. And, and then you met some people and saw movies and that made you want to be an astronaut beyond what we've already talked about hiring faster. Right you met folks who'd flown in space. Yes, I met Sally ride in grad school. Kind of a jaw dropping moment to get a grad school wall in the Air Force, by the way, while in the Air Force. Yes, therefore sent me to grad school. And Sally ride came through there and gave a speech and I went up to her and it was so stunned to meet her that I couldn't even talk. And then I also met Dick Covey he came to our graduation for test pilot school he was a graduation speaker. And, and he encouraged me to apply for the astronaut program. He basically said, we hope we see you in Houston sometime, which is where all the astronauts live and train. And, and I also had been primed by watching a couple of IMAX films. One of them, in particular the dream is alive narrated by Walter Cronkite. And, and that movie was something you saw in an IMAX theater, it still plays, you can get it on DVD if you want, and it is a real stunner. And, and when I looked out the window the first moments. The first thing I said, according to my crew mates was, this looks just like an IMAX movie, because it's about the only film format that does justice to the view. Yeah. So, so between the IMAX movies I saw and the people that I met, and then of course being encouraged and wanting to keep flying higher and faster it all came together, and I applied to the program and much to my amazement. I got selected, which I didn't expect it was sort of like winning the lottery in my head. Yep.  Because, because you're an exceptional person but also down to earth. Oops, there's a little play on words. But it's true. And it is true and you haven't gotten ahead of that. As you went through all of these firsts, first class with women at the Air Force Academy, first military woman in space, and many others were, if you weren't the first you're breaking a lot of ground in new territory for women. Did you think of it that way or did you think of it as individually as a person, I'm doing this, it's not about my gender, or was it both? I primarily was trying to do these things because they sounded like a whole lot of fun. And I, good motivation. Yeah, I, I mean, there's obviously more to it than that, but the more to it than that is I wanted to be a professional engineer. I wanted to do a job at it. I wanted to make, make my life's work, doing that in service to my country. And as it turns out, I happened to be first on some things but that's not why I did them. I didn't do them because of the gender I didn't do it because I'd get a chance to be first I did it because it's not like a lot of fun and timing is everything right fig. The doors opened about the time I was ready to step through them.  Well, you made the most of it. I have to ask you as an engineer do you have a slide roll back on your shelves that we see behind, because my dad was a chemist and my sisters were techies and we had a Facebook conversation about slide rolls, which I could never use. But do you, you know, they quit issuing those to the Air Force Academy cadets, the year before I got there. So, I don't have a slide roll never learned how to use one. I do have an advocate surrounding you somewhere. But, but yeah, they, they gave us a very expensive little calculator that could add subtract. A small nuclear reactor in it. So I'm going to leave a minute or two at the end to talk about your current segment but before we do that. I want to talk about something that was an, I think equals the accomplishments you made as a flight desk engineer and astronaut. And that's your return to the Air Force, because astronauts who are Air Force officers really leave the Air Force. They're off doing NASA things and most of them never come back. Right. Yeah, most of them do not come back. Right. Got a picture of you as a three stars the 14th Air Force commander.  And I think a really fine general and I don't say that about everybody wants it you know we have a little bit of hubris if you will about that. How, how was it to be out of the Air Force for so long as an astronaut and come back in in a senior and very responsible leadership role. Well, first of all, all the acronyms have changed. And the uniforms looked like they were a little different and the whole Air Force had reorganized. Oh yeah you missed that. And I left to go to NASA in 1990. So they changed everything folks from street addresses to regulation numbers. You have to have lived this to know how different the Air Force was when she came back. Yeah, but I knew when I went to go to NASA that I would come back to the Air Force. I just knew that I would do that because the Air Force was my DNA. And I have truly. Yeah, so I have exactly that being in the background there so I had to give so much credit to the people around me, who recognize that I was swimming underneath all the things that changed while I was gone. But people were so gracious.  They never minded helping me remember what side of the uniform to put my name tag. Because at NASA we only wear civilian clothes. We don't wear uniforms. So I, I, and I had a lot of very, very supportive people, you included Faye, who recognized, you know what situation I was in and was perfectly willing to help me work through all that. And, and of course, what the Air Force did in their great wisdom was they put me in the part of the space program that involved understanding satellites. And that was incredibly insightful of the Air Force to do that because I had been a satellite. And so I really intuitively fit in well with the mission area that they put me into, which we would, we called space control back then. Yep. So I'm forever grateful for the Air Force for recognizing that that's where my real value was was to help with that part of the mission. Well, I think your real value was you were an outstanding leader, and you did the right thing for both mission and people. And we saw that in you.  And now I want to make sure that we give you a chance to talk about what your current figment is in your, this iteration of your professional and personal life. Yes, I have to talk about the fact that all those years I was doing everything you've described. I missed out on so much with the family. So when I retired I said I'm going to move back near my parents and I'm going to be there for them. However long it needs to be. And that was seven plus years ago, and they're still with us. So I, I say my, my parents, my dad, he's 90 now. He still tells flying stories big. I have a future. I secretly record with my iPhone when he gets on a roll. So I have those stories. And, and as long as they're around that's, that's really my, my, my priority right now is to make sure that whatever it is they need on there to get it for. Are they proud of you at all. Oh no.  Yeah, by the way, yeah. Tell you all about me, just, even if you're sure. So, I'd love to hear it. And in my folks, sadly, passed away before much of my unexpected success and I wish they could share it I'm sure they do from above, even above where you were above. So, and your dad, by the way, was a rescue helicopter pilot in Vietnam, the most dangerous and selfless side of flying in the Vietnam war and you're right it's in your DNA. And I'm so thankful that we got a chance to share with folks. I know you've inspired and entertained. I hope you'll get, you'll get out to Hawaii now that things you get normal and visit the Rollies. I'll 100 and I will host you, because I can afford it with, with Ross's money from golf. And that's my story. And we look forward to seeing you and thank you for everything you've done for our country and for the world really Susan. Well thank you too, Fig, for everything you've done. It's been marvelous talking with you today and this is the great thing you're doing. Thank you. It's a lot of fun and we'll do it again next week folks and make sure you click like or share or whatever it is we do in the digital world and send me any ideas that you might have on our, on our topics. Aloha and Mahalo. Thanks Susan. Bye bye.


Hi, and welcome to my show. I sure hope you enjoyed my show on Ronnie Spector, one of the great performers of our country. Sadly, left us, but I sure enjoyed my interview with her tour manager, Dan Lillenfeld. Well, we're switching gears tonight for a very special show arranged by my dear friend, Rob Baldacci. If you've ever been to Las Vegas and you Google up the top five attractions out there, you will see that one of the top three is the so-called Mob Museum. And that museum is located in the downtown district of Las Vegas. And it contains memorabilia and artifacts and the entire history of organized crime in America and how it affected American culture both good and bad. Well, with me tonight are two people that were heavily involved in that. If you've seen the movie Casino, if you've seen the movie Goodfellas, if you've seen the movie The Godfather, you will understand the life that these two gentlemen lived because they were right in the thick of it. With me today is Bobby Luisi and Joe, Paul Tenzo. Gentlemen, thank you so much for coming here. Thank you for having us. Bobby, the first question I'm going to ask you right out of the blocks is, do you feel lucky to be alive? Actually, I do. When we came up in the 90s, there was a war on Boston. Yes. Well known. And a lot of people got killed. I lost some family members in that war. Yeah, I was lucky to survive it. I think it was God's will. And Bobby, you say you lost family members. You lost your father, you lost your brother, and you lost your cousin and a dear friend. Yes. They were taken out in a restaurant called the 99 Restaurant in Charlestown, Massachusetts. And do you remember the date when that happened? It was about 1995. And where were you on that day? Actually, I was supposed to be there. You were supposed to be there? I was supposed to be there with Vinnie and Damien, the shooters. And I had another appointment. And thank God I wasn't there, and I missed that. So Bobby, this is right out of the movies. This was a set up. They knew your dad was going to be there. No, they didn't work out like that. No. There was an altercation the night before. I see. And Vinnie and Damien came to me over it because they know how dangerous my father and my brother and that crew were. So I told them to stay out of the mart then. We were straightening it out because I had separate crew from my father on the street. I said, we're going to straighten it out. So they went to the 99 restaurant in Charleston out to stay out of the mart then, which is over the bridge. Right. And who walks in, my family. Wow. Unbelievable. This was a shooting, of course. Yeah. And were the perpetrators, were they found? Were they caught? Yes, they were. And did they serve time for that? Yeah. Damien Clemente and his father, Cleo, are doing life sentences for those murders. Are they still there, you mean? Yes.  They're still there. Yep. Paul, you mentioned to me that you're also from the North End. Yes. And you referred to Bobby as like a mentor. Yes.  And you also were what is referred to as a maid man. Yes. And how do you get to become a maid man? There's a ceremony. Right. That's the one involved. That's the one that was showed that, right? Yeah. Is that accurate? Pretty much what you have to see. Yeah. Bobby, you know, he knew I was by myself and running my own stuff at the time. And I was like a lone wolf. And he dragged me in and took care of me. He dragged me in with his crew. Bobby, this is the Boston crew. Yes. Where you guys were. The Boston crew. Right. So Rob, how did you, Rob, how did you get... Other than the fact that your name is Bob Atch. I have a vowel at the end of my name. That's the big K. And we're all related. We're a sympatica. That's all. All cool jeans. That's right. That's all. That's all you need to know. Well, what else do you need to know? Yeah. I'm not going to ask. The show is over, folks. Rob, when you and I went to F. Lee Bailey's Memorial Service in Boston. Yes. Very proud to be there. Went with our friend Steve Schwartz who's in the studio with us, folks. I just wanted to meet some of his clients. And apparently one of them is sitting with us today. That would be you, Paul. Yes. You were represented by F. Lee Bailey. Yes. Paul, why don't you talk a little bit about that? Yeah. My case pro bono. What was the case? When I was 21 years old, I was framed for two counts of murder of a drug deal gone wrong. But that wasn't the real story behind it. The real story was more of a setup. The people involved, you know, they were trying to shake down people and rob people for drugs or street money or whatever. They got executed, pretty much massacred. I think it was some of the bodies had like 36 entry and reentry wounds. And it was done by young guys. More or less, you know, teenagers, young teenagers, they were being bullied and scared and threatened and, you know, they had enough of it at one point. They were afraid for their lives and they did what they had to do. And my first trial I got found guilty on and I was serving a double natural life with a three to five on it. What does that mean, a double natural life? That means life with no parole. Life with no parole. First degree murder, yes. And you're a young boy, a young man. Yeah, I was 21 when I arrested. 21. And F. Lee Bailey represents you in the first trial. No. No, his partner, Kent Fishman. Yes. Okay, excuse me. So the first trial, you found guilt. Yes. What happened? I got sentenced to the natural life and everything. And I was up at Walpole, Cedar Junction, they call it. It was the worst prison at that time. Had the highest murder rate. They actually, the ones that made the standup count because that guy was dead in himself for three days and they kept moving the body to make it look like he was still alive. Jesus. Yeah. And so, F. Lee Bailey. It was an honorable place. And F. Lee Bailey comes along and represents you in another trial. Yes. He came and got me for my appeals and he told me he waited his whole life for my case. He said he waited for this case his whole life to have an innocent man, you know, on trial with a guilty man on the stand pointing fingers at people. The eyewitness was actually the real murderer. During the second trial, when I was found not guilty, he came in as the witness and they changed his plea bargain from excessive immunity from Rob accessory to Rob to immunity from murder because they knew we had more to do with it than what he was saying. So once they gave him immunity to murder, he actually confessed to the crimes and he said that makes the models the calibers of their handguns in the distance as they were fired from the from the victims. And when the jury heard that they were like a gasp and next thing you know, they told him, wow, if that's the truth, then we're going to try you for perjury. And he goes, oh, I changed my mind. Paul Tanzo did it. But the jury already heard it and the damage was done. The jury actually waited outside the courthouse crying, waiting for me. So when the verdict is announced, not guilty, what was the feeling you had? My knees buckled. Your knees buckled. Yeah. And tears immediately. My best friend, Tiziano, who also grew up in the North then was in there with me that day. And I mean, we're all in tears. And the next thing I know me and my family walk out of the courthouse and the jury's waiting for me. And it was like all hugs and kisses and everything. Was your life involved in that aspect of your life? Was that over as of that moment? I mean, did you stop any involvement? As soon as I was found not guilty, I stopped everything. I was engaged at the time and I applied to go to North Bennett Street Industrial School. It's the oldest trade school on the East Coast for jewelry making. Jewelry making. Yeah. And I made some beautiful. The airing actually. I like that. I like that. That's actually handmade. It's got a special class. I did it differently than regular airing. So, my friend, you went from being a made man. No, I wasn't made yet. Oh, you weren't made yet? No. I was like a hang around associate of another crew. Bobby at the time. Then you're making jewelry afterwards. And then Bobby, explain how you guys got together and Paul became a made man and then how you became a made man because you were proposed to be a made man under the Potriaca family, correct? Yes, I was. Why don't you kind of explain the connection there and how you did become a made man and how Paul did as well? Yeah. In the 90s, I came around. They had a good front from East Boston. I'm not going to say his name on the show. He was already a made guy in the Potriaca family. He proposed me in the family. Then the war broke out really heavy in Boston. A lot of people were getting killed. He ended up going to prison. He was my guy at the time. We're still very good close friends, like a brother. When that happened, I was having problems with the Potriacas on the street. I had my own crew. I was making my own money. It was getting really powerful. At one point, I decided to go to New York. Then I was let to Philadelphia. I became a proposed man in the Philadelphia family. That really doesn't happen often. But Joey's war was just getting over. My war was just getting over in Boston. The war that I was in. Everything was calming down at that point. Joey took me into the family. Joey Molino. And the Philly family. First name on my book radio, Joey Molino. He took me in. Made me. Shortly after that, I became a couple. Now, you've got to remember, I'm a Boston guy. Born and raised. I'm a Philly guy. I'm a Boston guy. I go back as a made guy. Shortly after that, we started training on my crew. The first ceremony we had in Boston, myself and another couple did that. And we took four guys in. And then the next one was three. So that's how we did it. So now I got a crew up in Boston. I'm a cop of regime in the Philadelphia family. And that made me a Boston Boston. Because I was 300 miles away from my family. And my crew was running an octet. And your name, you were referred to as Boston Bob. That was your name, Boston Bob. That's what they call me a Philly.  Yes. What did they call you in Boston? I didn't have a nickname in Boston. Yeah, but your name in Louisiana is well known. Because of your family, your father, very well known on the street. And tell me again, what year was it your father was assassinated? I think it was October 95? 95. Yeah, I think it was October 95. Well, and you were pretty much considered, you're referred to in the literature about you, as the Philly Point Man. So are you going back and forth between Philadelphia and Boston? Is that what you're doing during that time?  Okay. When I got away, one of my cousins was the acting captain. And I got away as much as I could to be honest with you. I love the Philly guys and hanging around down there. The first agreement I made with Joey is that Boston and Philly weren't going to intermingle business so we didn't roll with state lines. So I was running Boston under my crew the way I wanted to and Joey was at Philly. What kind of role did the Patriaca family have when you were a Boston boss? Well, they were in disarray. They were. And they were trying to put their family back together at that time. So I took advantage of it to tell you the truth. I mean, I could have never did this in the 80s if Raymond was around, Jerry and Julo or Joe Russo. These are all the big names in Boston. I could have never done one of them. I would have been with them anyway. It's just the way things out turned out in the war that I ended up with Philadelphia. Bobby, whenever anybody hears about the Boston mob, the Boston mafia, the Boston cosinostra, the name comes up all the time, of course, other than Patriaca, of course, is Whitey Bulger who was found after so many years on the run. And there is one sentence that I want to talk to you about that is in your list of people. If you Google this man's name up, you get to take 20 pages. But one of the sentences that comes up that says that at one point in time, you try to, quote, gain control of the Whitey Bulger interests in Boston. Is that true that you... Well, let me tell you what happened. I went up and took over the guys in Somerville. Okay. They were the remnants of the Winter Hill gang. They were the young guys, which were my age. So I was up in Ball Square, and I took that crew under my umbrella. While I was doing that, there were some people in Somerville, a method that were with those guys and that we were grabbing them. During that time, when they were gone, we grabbed Kevin Weeks, and he was with Whitey and Stevie Fleming, and we did a little business with Kevin. I actually really liked Kevin, you know? So we were just not really trying to take it from them because the Montoranos, they're all in prison. I don't agree with that. I didn't really want to take any Whitey or Stevie stuff, but what was out there, like Stevie Fleming's son was working with me, Billy. So what was out there, I was gathering them in under my umbrella as much as I could. Did both of you know Whitey Balger? Did you know him also? I know him personally. I never met him. Get out of here. Never met him. So all the times that you're in Boston dealing with all this sort of stuff, they make this reference to you, but pretty much trying to hone in on his territory, and you're saying you never actually met him. Never met him. Really? I met Stevie Fleming, but I never met him. Stevie Fleming was right in there. You have to understand Whitey Balger himself was an associate. Whitey Balger was a bad man. Yes. I was going to ask you. He's a gangsta's gangsta, besides what he did. Right. You can't take that away from him. And I'm not going to try to. Right. But he wasn't Italian. Right. He was a part of that thing. He was an associate. So no Italian ties, like his mother wasn't Italian? No, no. All Irish. No, all Irish. But he was at the time, he was with the Angelo family. Okay. Him and Stevie Fleming with Petriaca. Yeah. They were at a time with them. Now, gentlemen, both of you served time. You did how many years before you? I think it was three years before they released me on bail for the murder charges. Right. Then I think I got bomb-making explosives or something in federal time. And then my other charges, gun charges. Yeah. Would you do a total on that? Yeah. I don't know. My whole life, I totaled like eight years and probably like 16 on probation. And Bobby, your term was a sentence of 20, but you served 14. What happened? Right. I went to trial. Yeah. Talk about that, Bob. Yeah. I went to trial. I think it was 2001, my first trial around that date and an animus guilty right down the line. Because everybody in Boston, they all know who I was. I was all over the papers. Right. You know, so they just found me guilty right away. Right. So I got 235 months. I go back. Some miracle happened and they changed the guidelines. I end up winning an appeal. I'm the first one that turned over a drug case in Boston. I guess people are still using that case now because we won a trial on a trap. So I win that. We go back to trial. In 2007, I might have to, I'm not good with dates. And I lost again, but they took 47 months off. I see. Yeah. I went to 15-8. Right. And I served 13-9. 13-9. Yeah. And where did you serve that? Oh, I was all over. I was with the New Jersey. I was in MCC in New York when 9-11 hit. When 9-11, you were right there? I was right there. Wow. Oh, my God. You had jets going all over? Right. We were locked down for over a month. You were locked down for a month? And you're not being told what, you don't know what's going on. I mean, just when you're seeing on the news. Well, luckily we had TVs. Yeah. And some of the guys had TV antennas, the cable, everything went out when the second tower went down. That's right. We lost our phones, everything. And we were watching everything on TV. We knew everything that was happening. Right. Paul and Bobby, when you're serving time, we've seen everybody that's our age has seen all the prison movies from, whatever, you know. When you're serving time, because they know that you were so-called made, are you any more protected than anybody else? In other words, do you have any less fear while you're in prison than anybody else because of your connections? Well, you have to understand something. In prison, in federal prison, in state prisons, if you go home with a reputation, guys respect that. Okay. Okay, so going in as a boss, you know, some guys are oppressed with it. I remember when I first went in, they were bringing me in food. You know, all the prisoners. Bobby, I got this. I got a radio. You could use that. You know, they're really good. Really good like that. I fought a lot in prison. I'm not going to lie to you. You did really? Oh, yes, I did. But mostly I got the respect in the units. Okay. Same with you, Paul? When I went in as a soldier, you know, I had less respect than Bobby from like all these other big mobsters and stuff like that. And I got into a lot more fights.  Now, do you have to like protect yourself? Oh, yes. I actually got kicked out of OCC Supermax for fighting. I got into three fights in one month. And I got kicked out of prison. Did you ever serve time in the hole or in a... Oh, yeah, plenty of... Oh, really? Every time I get into the fight, every time in the hole. Really? Three, four months at a time. Oh, my God. I was there for six months straight. They kept coming by and waking me up every day to get out of bed and make my bed. And I'm like, I'm not moving. And they call the extraction team. And then they do it again the next time I got out of the hole. And then the third time, when they try to wake me up and they call the extraction team to come and get me, the extraction team said, just let him sleep. Leave him alone with them doing this. They just left me alone. Bobby, when they discuss the things that you were doing in this part of organized crime, they list several things. One of them is bookmaking. What is bookmaking for the edification of the audience? What's bookmaking? Well, we used to have offices. You know, you're calling for football games, basketball games, hockey games, baseball games. It's all gambling. Right. So we would take the action on the phones. Okay. You know, and when I lose, we'd settle up once a week with you. Okay. You know, we were hoping you'd lose every week. I was a terrible bookmaker. Why were you terrible at it? I don't know. I just, it bored me. You know, I was a gambler. I was a gambler. I gambled a lot. You did? You know, I liked that side of the action, sitting and taking it. Right. Okay. So I always had people doing that. Okay. So gambling is, you're basically dealing with illegal gambling. Absolutely. But there's really, quote, no victims.  I assume, did that happen a lot, by the way? Yes. It happened, but not as much as you think. Okay.  The other one is extortion. Yes, exactly. How do you define that? Well, in the old days, they used to extort people in the neighborhood that own grocery stores and bakeries and that. Right. We didn't do that. Yeah. I always felt, and we felt in Manhattan, that was our neighborhood, and we had to protect it. Right. Did you actually help a lot of the restaurants? Yeah, we did. My thing was, I would extort drug dealers, bookmakers, and loan sharks. Okay. So now what you say, you say that if you're going to do business in this area, you've got to give us a piece of the action. Is that how that works? Absolutely. I never bothered in this area of people. What about construction unions? Yeah. Well, my extent in the union, there was a little power there. The teams did union. Right. A lot of my guys were getting jobs in there. Yeah. And I had some good people that were in the unions at the time. Yeah. I was able to get people jobs and certain things like that, but I never got deeper to the union and to extort them. Prostitution. Yeah. I never did that. No. Really? There were two things I wouldn't do on the street. I wouldn't be a pimp and I wouldn't sell heroin. Okay. Right. So as we know, the theme of the movie The Godfather, which some people write is the best movie ever done. Absolutely. Mylon Brando, Vito Corleone, gets in trouble with the other families because he says, I'm not going to get involved with heroin. Right. And of course, the five families want to get into it because that's the whole thing that happens in Godfather III. And you're saying that you had this, quote, integrity to not get involved in those particular aspects. When I was coming up in the 90s, I mean, cocaine is a bad drug. Yeah. And I'm ashamed that I had anything to do with it. Yeah.  I was selling kilos. Really? Yeah. Okay. Even as far as Wall's main. And we'll talk about that later. Yeah. Yes. Yeah. So that was my big business. But I've seen it as a social drug. Well. Harrowing. Percadence. Right. Those oxycons. Yeah. Killing people. Yeah. And people needed to get out of bed in the morning. By the way, I was talking to an alcohol counselor one day and talking about her job. And you know what she said to me? She said, by the way, she said, do you know one of the easiest drugs to quit is cocaine? Yeah. I saw the TV shows about people that got hooked on it and so on and so forth back in the 90s and the early 2000s. So Paul, what were you doing? What kind of activities were you involved in? I'm growing up. I used to sell fireworks for the, more basically kind of the playground when I was younger as a teenager. I pulled a lot of scores. I sold a lot of cocaine in part. Ecstasy when it hit big. I think that was the only pill I ever sold. Like he said, those other things were taboo, prostitution and heroin and that stuff was always taboo and our neighborhood was frowned upon. And what about loan-shocking? What's that involved? Loan-shocking. Well, you know, there's a wide variety of people that you deal with. I made a lot of money with loan-shocking.  Right. You can't pay your rent. You need $1,000 to pay you.  Whatever the reason is. Outlaw. Whatever. So you come to me and if I knew you well, I'd give it to you for three points. So you gave me $30 a week till you paid the principal back. Okay. If you weren't a friend, there was five points. You gave me $50 a week on that thousand. At my peak, I had $650,000 on the street. Wow. So you got that out there. Yeah. And people paying, quote, interest on that every week. Yeah. But a lot of the money I gave out now to a main guy, if Paul he wanted a loan shock, somebody money, he'd come to me. I'd give it to him for a point. Okay.  So now, yes, I had $650,000 on the street. I averaged the vote $8,000 a week. Oh, my goodness. Yeah. Paul, you were making millions that weren't you? Yes. Yeah, when you were rolling between the drugs and the loan shock was my big thing. Yeah.  So where are you keeping all this money? Where were you keeping it in the box somewhere? Well, to tell you the truth, I got arrested in June of 1999. In March of 1998, I had a spiritual experience, we'll call it. And I started going back to church. Bobby, this was before you got arrested? Yes. It was before I got arrested. Correct. 15 months before. Talk about that. So I have this spiritual, the encounter. Yeah. It wasn't a way. It was an encounter lasted over eight hours with a demonic being. I've seen people that I'll say had been killed in the war. We'll put it that way. I've seen a lot of my evils. Finally, I had a call that night. I had a call. My mother had a church in East Boston. So I called the pastor, came up. My mother, everybody came up. I took the host that night. So I was sneak at the church when I was a boss. And nobody knew. There was a May guy in my crew, Tommy Caruso. Garrest. Garrest has slowly passed away now. But he was coming to church too. And he was a believer. And he became born again. And we both became born again at that church. And Bobby, you became a pastor in Memphis, Tennessee. And we were running out of time. It can't be half hour gone by like in three seconds. But you actually had a broadcast in Memphis. Channel 6 in Memphis. Channel 6 in Memphis.  I was in a black church. Yeah. Faith Keepers Ministry. And you were like the head pastor? No, no, no. Bishop Coleman was my next door neighbor. He read my book, God's Plan Revealed. It's up on Amazon right now. Yes, it is. He said, Bobby, I was Alonzo there. He said Alonzo, why don't you come and teach at my church? You had a different name. Alonzo Esposito. Esposito. So you went under the name of Alonzo Esposito for how long in your life? From 2013 to like changing to what, 17? Yeah, we would change together. You came down to Florida and got me. Yeah, because I was in Fort Lauderdale. I went and picked him up right away. It was 2017. I found him on Facebook. I see this picture and I was dying because we lost track of him. Sure. Because me moving around.  I mean in the positive sense. Oh, I know. And you go into witness protection and you, and next thing you find each other on Facebook, Twitter, whatever, that is just fascinating. Ladies and gentlemen, we run out of this. We run out of time. First half hour. But we're going to continue this show with me on five. I'm going to discuss some of the connections to Maine that occurred back in the days when organized crime was very big in Boston. And we're going to find out how it affected Maine when we have the me on five show. Thank you for watching this show. Gentlemen, thank you for being here. Rob, thanks for being the co-host. All right, buddy. Thank you. And we'll see you next month on the Dairy Runlet Show. Thank you.


One, background, several international guidelines recommend consuming two servings of fruits and three servings of vegetables per day, but their intake is low worldwide. We aim to determine the extent to which this low intake is related to availability and affordability. Two, methods, we assessed fruit and vegetable consumption using data from country-specific, validated semi-quantitative food frequency questionnaires in the prospective urban rural epidemiology, pure study, which enrolled participants from communities in 18 countries between January 1, 2003 and December 31, 2013. We documented household income data from participants in these communities. We also recorded the diversity and non-sale prices of fruits and vegetables from grocery stores and marketplaces between January 1, 2009 and December 31, 2013. We determined that. This article was authored by Victoria Miller, B.Sc., Professor Selene Youssef, D.Phil, Clara K. Chow, Ph.D., and others. We are article.tv, links in the description below.