Hardware Accelerated Molecular Docking: A Survey
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paper_title: Molecular docking algorithms.

paper_content:

By means of virtual screening of small molecules databases it is possible to identify new potential inhibitors against a target of interest. Molecular docking is a computer simulation procedure to predict the conformation of a receptor-ligand complex. Each docking program makes use of one or more specific search algorithms, which are the methods used to predict the possible conformations of a binary complex. In the present review we describe several molecular-docking search algorithms, and the programs which apply such methodologies. We also discuss how virtual screening can be optimized, describing methods that may increase accuracy of the simulation process, with relatively fast docking algorithms.
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paper_title: CHARMM: A program for macromolecular energy, minimization, and dynamics calculations

paper_content:

CHARMM (Chemistry at HARvard Macromolecular Mechanics) is a highly flexible computer program which uses empirical energy functions to model macromolecular systems. The program can read or model build structures, energy minimize them by first- or second-derivative techniques, perform a normal mode or molecular dynamics simulation, and analyze the structural, equilibrium, and dynamic properties determined in these calculations. The operations that CHARMM can perform are described, and some implementation details are given. A set of parameters for the empirical energy function and a sample run are included.
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paper_title: Multiple automatic base selection: Protein–ligand docking based on incremental construction without manual intervention

paper_content:

A possible way of tackling the molecular docking problem arising in computer- aided drug design is the use of the incremental construction method. This method consists of three steps: the selection of a part of a molecule, a so- called base fragment, the placement of the base fragment into the active site of a protein, and the subsequent reconstruction of the complete drug molecule. Assuming that a part of a drug molecule is known, which is specific enough to be a good base fragment, the method is proven to be successful for a large set of docking examples. In addition, it leads to the fastest algorithms for flexible docking published so far. In most real-world applications of docking, large sets of ligands have to be tested for affinity to a given protein. Thus, manual selection of a base fragment is not practical. On the other hand, the selection of a base fragment is critical in that only few selections lead to a low-energy structure. We overcome this limitation by selecting a representative set of base fragments instead of a single one. In this paper, we present a set of rules and algorithms to automate this selection. In addition, we extend the incremental construction method to deal with multiple fragmentations of the drug molecule. Our results show that with multiple automated base selection, the quality of the docking predictions is almost as good as with one manually preselected base fragment. In addition, the set of solutions is more diverse and alternative binding modes with low scores are found. Although the run time of the overall algorithm increases, the method remains fast enough to search through large ligand data sets.
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paper_title: Molecular docking: a problem with thousands of degrees of freedom

paper_content:

This paper reports on the problem of docking a highly flexible small molecule to the pocket of a highly flexible receptor macromolecule. The prediction of the intermolecular complex is of vital importance for the development of new therapeutics as docking can alter the chemical behavior of the receptor macromolecule. We first present current methods for docking, which have several limitations. Some of these methods consider only the flexibility of the ligand solving a problem with a few tens of degrees of freedom. When the receptor flexibility is taken into account several hundreds or even thousands of degrees of freedom need to be considered. Most methods take into account only a small number of these degrees of freedom by using chemical knowledge specific to the problem. We show how to use a singular value decomposition of molecular dynamics trajectories to automatically obtain information about the global flexibility of the receptor and produce interesting conformations that can be used for docking purposes.
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paper_title: Knowledge-based scoring function to predict protein–ligand interactions

paper_content:

The development and validation of a new knowledge-based scoring function (DrugScore) to describe the binding geometry of ligands in proteins is presented. It discriminates efficiently between well-docked ligand binding modes (root-mean-square deviation <2.0 A with respect to a crystallographically determined reference complex) and those largely deviating from the native structure, e.g. generated by computer docking programs. Structural information is extracted from crystallographically determined protein-ligand complexes using ReLiBase and converted into distance-dependent pair-preferences and solvent-accessible surface (SAS) dependent singlet preferences for protein and ligand atoms. Definition of an appropriate reference state and accounting for inaccuracies inherently present in experimental data is required to achieve good predictive power. The sum of the pair preferences and the singlet preferences is calculated based on the 3D structure of protein-ligand binding modes generated by docking tools. For two test sets of 91 and 68 protein-ligand complexes, taken from the Protein Data Bank (PDB), the calculated score recognizes poses generated by FlexX deviating <2 A from the crystal structure on rank 1 in three quarters of all possible cases. Compared to FlexX, this is a substantial improvement. For ligand geometries generated by DOCK, DrugScore is superior to the "chemical scoring" implemented into this tool, while comparable results are obtained using the "energy scoring" in DOCK. None of the presently known scoring functions achieves comparable power to extract binding modes in agreement with experiment. It is fast to compute, regards implicitly solvation and entropy contributions and produces correctly the geometry of directional interactions. Small deviations in the 3D structure are tolerated and, since only contacts to non-hydrogen atoms are regarded, it is independent from assumptions of protonation states.
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paper_title: DOCK 4 . 0 : Search strategies for automated molecular docking of flexible molecule databases

paper_content:

In this paper we describe the search strategies developed for docking flexible molecules to macomolecular sites that are incorporated into the widely distributed DOCK software, version 4.0. The search strategies include incremental construction and random conformation search and utilize the existing Coulombic and Lennard-Jones grid-based scoring function. The incremental construction strategy is tested with a panel of 15 crystallographic testcases, created from 12 unique complexes whose ligands vary in size and flexibility. For all testcases, at least one docked position is generated within 2 A of the crystallographic position. For 7 of 15 testcases, the top scoring position is also within 2 A of the crystallographic position. The algorithm is fast enough to successfully dock a few testcases within seconds and most within 100 s. The incremental construction and the random search strategy are evaluated as database docking techniques with a database of 51 molecules docked to two of the crystallographic testcases. Incremental construction outperforms random search and is fast enough to reliably rank the database of compounds within 15 s per molecule on an SGI R10000 cpu.
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paper_title: Development and Validation of a Genetic Algorithm for Flexible Docking

paper_content:

Prediction of small molecule binding modes to macromolecules of known three-dimensional structure is a problem of paramount importance in rational drug design (the “docking” problem). We report the development and validation of the program GOLD (Genetic Optimisation for Ligand Docking). GOLD is an automated ligand docking program that uses a genetic algorithm to explore the full range of ligand conformational flexibility with partial flexibility of the protein, and satisfies the fundamental requirement that the ligand must displace loosely bound water on binding. Numerous enhancements and modifications have been applied to the original technique resulting in a substantial increase in the reliability and the applicability of the algorithm. The advanced algorithm has been tested on a dataset of 100 complexes extracted from the Brookhaven Protein DataBank. When used to dock the ligand back into the binding site, GOLD achieved a 71% success rate in identifying the experimental binding mode.
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paper_title: A Second Generation Force Field for the Simulation of Proteins, Nucleic Acids, and Organic Molecules

paper_content:

We present the derivation of a new molecular mechanical force field for simulating the structures, conformational energies, and interaction energies of proteins, nucleic acids, and many related organic molecules in condensed phases. This effective two-body force field is the successor to the Weiner et al. force field and was developed with some of the same philosophies, such as the use of a simple diagonal potential function and electrostatic potential fit atom centered charges. The need for a 10-12 function for representing hydrogen bonds is no longer necessary due to the improved performance of the new charge model and new van der Waals parameters. These new charges are determined using a 6-31G* basis set and restrained electrostatic potential (RESP) fitting and have been shown to reproduce interaction energies, free energies of solvation, and conformational energies of simple small molecules to a good degree of accuracy. Furthermore, the new RESP charges exhibit less variability as a function of the molecular conformation used in the charge determination. The new van der Waals parameters have been derived from liquid simulations and include hydrogen parameters which take into account the effects of any geminal electronegative atoms. The bonded parameters developed by Weiner et al. were modified as necessary to reproduce experimental vibrational frequencies and structures. Most of the simple dihedral parameters have been retained from Weiner et al., but a complex set of 4 and yj parameters which do a good job of reproducing the energies of the low-energy conformations of glycyl and alanyl dipeptides has been developed for the peptide backbone.
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paper_title: Glide: A New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy

paper_content:

Unlike other methods for docking ligands to the rigid 3D structure of a known protein receptor, Glide approximates a complete systematic search of the conformational, orientational, and positional space of the docked ligand. In this search, an initial rough positioning and scoring phase that dramatically narrows the search space is followed by torsionally flexible energy optimization on an OPLS-AA nonbonded potential grid for a few hundred surviving candidate poses. The very best candidates are further refined via a Monte Carlo sampling of pose conformation; in some cases, this is crucial to obtaining an accurate docked pose. Selection of the best docked pose uses a model energy function that combines empirical and force-field-based terms. Docking accuracy is assessed by redocking ligands from 282 cocrystallized PDB complexes starting from conformationally optimized ligand geometries that bear no memory of the correctly docked pose. Errors in geometry for the top-ranked pose are less than 1 A in nearly half of the cases and are greater than 2 A in only about one-third of them. Comparisons to published data on rms deviations show that Glide is nearly twice as accurate as GOLD and more than twice as accurate as FlexX for ligands having up to 20 rotatable bonds. Glide is also found to be more accurate than the recently described Surflex method.
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paper_title: AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading

paper_content:

AutoDock Vina, a new program for molecular docking and virtual screening, is presented. AutoDock Vina achieves an approximately two orders of magnitude speed-up compared to the molecular docking software previously developed in our lab (AutoDock 4), while also significantly improving the accuracy of the binding mode predictions, judging by our tests on the training set used in AutoDock 4 development. Further speed-up is achieved from parallelism, by using multithreading on multi-core machines. AutoDock Vina automatically calculates the grid maps and clusters the results in a way transparent to the user.
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paper_title: A semiempirical free energy force field with charge-based desolvation

paper_content:

The authors describe the development and testing of a semiempirical free energy force field for use in AutoDock4 and similar grid-based docking methods. The force field is based on a comprehensive thermodynamic model that allows incorporation of intramolecular energies into the predicted free energy of binding. It also incorpo- rates a charge-based method for evaluation of desolvation designed to use a typical set of atom types. The method has been calibrated on a set of 188 diverse protein-ligand complexes of known structure and binding energy, and tested on a set of 100 complexes of ligands with retroviral proteases. The force field shows improvement in redock- ing simulations over the previous AutoDock3 force field.
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paper_title: A general and fast scoring function for protein-ligand interactions: a simplified potential approach.

paper_content:

A fast, simplified potential-based approach is presented that estimates the protein-ligand binding affinity based on the given 3D structure of a protein-ligand complex. This general, knowledge-based approach exploits structural information of known protein-ligand complexes extracted from the Brookhaven Protein Data Bank and converts it into distance-dependent Helmholtz free interaction energies of protein-ligand atom pairs (potentials of mean force, PMF). The definition of an appropriate reference state and the introduction of a correction term accounting for the volume taken by the ligand were found to be crucial for deriving the relevant interaction potentials that treat solvation and entropic contributions implicitly. A significant correlation between experimental binding affinities and computed score was found for sets of diverse protein-ligand complexes and for sets of different ligands bound to the same target. For 77 protein-ligand complexes taken from the Brookhaven Protein Data Bank, the calculated score showed a standard deviation from observed binding affinities of 1.8 log Ki units and an R2 value of 0.61. The best results were obtained for the subset of 16 serine protease complexes with a standard deviation of 1.0 log Ki unit and an R2 value of 0.86. A set of 33 inhibitors modeled into a crystal structure of HIV-1 protease yielded a standard deviation of 0.8 log Ki units from measured inhibition constants and an R2 value of 0.74. In contrast to empirical scoring functions that show similar or sometimes better correlation with observed binding affinities, our method does not involve deriving specific parameters that fit the observed binding affinities of protein-ligand complexes of a given training set. We compared the performance of the PMF score, Böhm's score (LUDI), and the SMOG score for eight different test sets of protein-ligand complexes. It was found that for the majority of test sets the PMF score performs best. The strength of the new approach presented here lies in its generality as no knowledge about measured binding affinities is needed to derive atomic interaction potentials. The use of the new scoring function in docking studies is outlined.
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paper_title: Fast docking using the CHARMM force field with EADock DSS

paper_content:

The prediction of binding modes (BMs) occurring between a small molecule and a target protein of biological interest has become of great importance for drug development. The overwhelming diversity of needs leaves room for docking approaches addressing specific problems. Nowadays, the universe of docking software ranges from fast and user friendly programs to algorithmically flexible and accurate approaches. EADock2 is an example of the latter. Its multiobjective scoring function was designed around the CHARMM22 force field and the FACTS solvation model. However, the major drawback of such a software design lies in its computational cost. EADock dihedral space sampling (DSS) is built on the most efficient features of EADock2, namely its hybrid sampling engine and multiobjective scoring function. Its performance is equivalent to that of EADock2 for drug-like ligands, while the CPU time required has been reduced by several orders of magnitude. This huge improvement was achieved through a combination of several innovative features including an automatic bias of the sampling toward putative binding sites, and a very efficient tree-based DSS algorithm. When the top-scoring prediction is considered, 57% of BMs of a test set of 251 complexes were reproduced within 2 A RMSD to the crystal structure. Up to 70% were reproduced when considering the five top scoring predictions. The success rate is lower in cross-docking assays but remains comparable with that of the latest version of AutoDock that accounts for the protein flexibility. © 2011 Wiley Periodicals, Inc. J Comput Chem, 2011
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paper_title: Reconfigurable Computing: The Theory and Practice of FPGA-Based Computation

paper_content:

The main characteristic of Reconfigurable Computing is the presence of hardware that can be reconfigured to implement specific functionality more suitable for specially tailored hardware than on a simple uniprocessor. Reconfigurable computing systems join microprocessors and programmable hardware in order to take advantage of the combined strengths of hardware and software and have been used in applications ranging from embedded systems to high performance computing. Many of the fundamental theories have been identified and used by the Hardware/Software Co-Design research field. Although the same background ideas are shared in both areas, they have different goals and use different approaches.This book is intended as an introduction to the entire range of issues important to reconfigurable computing, using FPGAs as the context, or "computing vehicles" to implement this powerful technology. It will take a reader with a background in the basics of digital design and software programming and provide them with the knowledge needed to be an effective designer or researcher in this rapidly evolving field. ::: ::: · Treatment of FPGAs as computing vehicles rather than glue-logic or ASIC substitutes ::: · Views of FPGA programming beyond Verilog/VHDL ::: · Broad set of case studies demonstrating how to use FPGAs in novel and efficient ways
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paper_title: CUDA by Example: An Introduction to General-Purpose GPU Programming

paper_content:

CUDA by Example: An Introduction to General-Purpose GPU Programming Jason Sanders and Edward Kandrot ISBN-13: 978-0131387683 Addison-Wesley Professional; 1 edition (July 29, 2010) Introduction This book is designed for readers who are interested in studying how to develop general parallel applications on graphics processing unit (GPU) by using CUDA C. CUDA C is a programming language, which combines industry standard programming C language and some more features which can exploit CUDA architecture. With proper introduction to NVIDA's CUDA architecture and in depth explanation for setting up development environment, this book is an easy to read, easy to understand, and hands on book. Readers of this book are assumed to have at least C language as background. Through this book, readers will not only gain experience in CUDA C development languages, but also will understand a lot of important underlying hardware knowledge, which in return can help software developers develop more efficient and effective applications. Outline of the Book This book is very well organized. Each chapter consists of general introduction, chapter objectives and Chapter Review. Both Sanders and Kandrot are senior software engineers in the CUDA Platform group and CUDA Algorithm team in NVIDIA Company, respectively. First chapter provide users background about history of GPU and CUDA architecture. Special features in CUDA architecture enable GPU to perform general purpose computation in addition to carry out traditional graphic computation. Readers can easily understand the benefit of CUDA architecture by reading though three different applications varying from medical field to environmental filed. In Chapter 2, Sanders and Kandrot equip users with complete lists of hardware and software support for running CUDA C applications. All software can be downloaded for free from websites suggested from authors. Then by a familiar Hello world program in Chapter 3, authors demystified that the CUDA C fundamentally is a standard C language with additional features which can allow application developer to specify which code can be run on device (GPU and its memory) or host (CPU and system memory). After setting all of proper background, the use of CUDA C to run parallel programs on GPU are discussed from Chapter 4 to Chapter 7. In Chapter 8, authors try to illustrate how to incorporate rendering and general purpose computation by using CUDA C. Readers without background in OpenGL or DirectX, can skip this chapter and go to the next. However, this chapter is a great addition to the book since it gives readers complete view of CUDA C. Even though CUDA C turns complicated application with single thread execution into easier case by parallel processing, there are some situation that special care should be taken when simple single thread application are tried to implement on massively parallel architecture; Chapter 9 discusses this topic. Compared to parallelism discussed in above chapters, which refers to parallel execution of a function on different sets of data, in Chapter 10, readers are exposed to a different class of parallelism on GPU, which refers to two or more completely independent tasks to be performed in parallel. Chapter 11 covers how to develop CUDA C application on Multiple GPUS. For further study, Chapter 12 shows more tools to aid CUDA C development and more resources to enhance reader's CUBA C development skills to another level. Summary Jason Sanders and Edward Kandrot wrote this book in such a way that is very easy to read and follow. Also, Sanders and Kandrot never forget the great sense of humor throughout the book. Reading this book is not only a discovery about CUDA C but also a joyful journal. It is highly recommended for students who are interested to learn CUDA C application development as Computer Science major. This book is recommended to be adopted as textbook for undergraduate students studying parallel programming. Jie Cheng, University of Hawaii Hilo
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paper_title: FPGA acceleration of rigid-molecule docking codes

paper_content:

Modelling the interactions of biological molecules, or docking, is critical both to understanding basic life processes and to designing new drugs. The field programmable gate array (FPGA) based acceleration of a recently developed, complex, production docking code is described. The authors found that it is necessary to extend their previous three-dimensional (3D) correlation structure in several ways, most significantly to support simultaneous computation of several correlation functions. The result for small-molecule docking is a 100-fold speed-up of a section of the code that represents over 95% of the original run-time. An additional 2% is accelerated through a previously described method, yielding a total acceleration of 36× over a single core and 10× over a quad-core. This approach is found to be an ideal complement to graphics processing unit (GPU) based docking, which excels in the protein-protein domain.
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paper_title: FPGA acceleration of rigid-molecule docking codes

paper_content:

Modelling the interactions of biological molecules, or docking, is critical both to understanding basic life processes and to designing new drugs. The field programmable gate array (FPGA) based acceleration of a recently developed, complex, production docking code is described. The authors found that it is necessary to extend their previous three-dimensional (3D) correlation structure in several ways, most significantly to support simultaneous computation of several correlation functions. The result for small-molecule docking is a 100-fold speed-up of a section of the code that represents over 95% of the original run-time. An additional 2% is accelerated through a previously described method, yielding a total acceleration of 36× over a single core and 10× over a quad-core. This approach is found to be an ideal complement to graphics processing unit (GPU) based docking, which excels in the protein-protein domain.
---
paper_title: A semiempirical free energy force field with charge-based desolvation

paper_content:

The authors describe the development and testing of a semiempirical free energy force field for use in AutoDock4 and similar grid-based docking methods. The force field is based on a comprehensive thermodynamic model that allows incorporation of intramolecular energies into the predicted free energy of binding. It also incorpo- rates a charge-based method for evaluation of desolvation designed to use a typical set of atom types. The method has been calibrated on a set of 188 diverse protein-ligand complexes of known structure and binding energy, and tested on a set of 100 complexes of ligands with retroviral proteases. The force field shows improvement in redock- ing simulations over the previous AutoDock3 force field.
---
paper_title: Fast docking using the CHARMM force field with EADock DSS

paper_content:

The prediction of binding modes (BMs) occurring between a small molecule and a target protein of biological interest has become of great importance for drug development. The overwhelming diversity of needs leaves room for docking approaches addressing specific problems. Nowadays, the universe of docking software ranges from fast and user friendly programs to algorithmically flexible and accurate approaches. EADock2 is an example of the latter. Its multiobjective scoring function was designed around the CHARMM22 force field and the FACTS solvation model. However, the major drawback of such a software design lies in its computational cost. EADock dihedral space sampling (DSS) is built on the most efficient features of EADock2, namely its hybrid sampling engine and multiobjective scoring function. Its performance is equivalent to that of EADock2 for drug-like ligands, while the CPU time required has been reduced by several orders of magnitude. This huge improvement was achieved through a combination of several innovative features including an automatic bias of the sampling toward putative binding sites, and a very efficient tree-based DSS algorithm. When the top-scoring prediction is considered, 57% of BMs of a test set of 251 complexes were reproduced within 2 A RMSD to the crystal structure. Up to 70% were reproduced when considering the five top scoring predictions. The success rate is lower in cross-docking assays but remains comparable with that of the latest version of AutoDock that accounts for the protein flexibility. © 2011 Wiley Periodicals, Inc. J Comput Chem, 2011
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paper_title: A Parallel Molecular Docking Approach Based on Graphic Processing Unit

paper_content:

Molecular docking technology is an important tool in molecular recognition and structure prediction for the protein-protein complex. In this work, based on the analysis of the widely used FFT-based method, we proposed a GPU parallel molecular docking program. The new docking program was applied to dock 5 protein-protein complexes of enzyme/inhibitor type. Docking results indicate that the parallel codes can make a good prediction of the given complex structures. Moreover, the GPU docking method can achieve a speedup of more than 3 times. Finally, we analyzed the influence of parameters for the docking results and parallel speedup. The obtained high quality parallel speedup and efficiency show the promising improvement of our method for future applications on structure prediction of protein-protein complex.
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paper_title: Porting Autodock to CUDA

paper_content:

This paper is a report on the migration of the molecular docking application, “Autodock” to NVIDIA CUDA. Autodock is a Drug Discovery Tool that uses a Genetic Algorithm to find the optimal docking position of a ligand to a protein. Speedup of Autodock greatly benefits the drug discovery process. In this paper, we show how significant speed up of Autodock can be achieved using NVIDIA CUDA. This paper describes the strategy of porting the Genetic Algorithm to CUDA. Three different parallel design alternatives are discussed. The resultant implementation features ∼50x speedup on the fitness function evaluation and 10x to 47x speedup on the core genetic algorithm.
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paper_title: MolDock: A New Technique for High-Accuracy Molecular Docking

paper_content:

In this article we introduce a molecular docking algorithm called MolDock. MolDock is based on a new heuristic search algorithm that combines differential evolution with a cavity prediction algorithm. The docking scoring function of MolDock is an extension of the piecewise linear potential (PLP) including new hydrogen bonding and electrostatic terms. To further improve docking accuracy, a re-ranking scoring function is introduced, which identifies the most promising docking solution from the solutions obtained by the docking algorithm. The docking accuracy of MolDock has been evaluated by docking flexible ligands to 77 protein targets. MolDock was able to identify the correct binding mode of 87% of the complexes. In comparison, the accuracy of Glide and Surflex is 82% and 75%, respectively. FlexX obtained 58% and GOLD 78% on subsets containing 76 and 55 cases, respectively.
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paper_title: An ant colony optimization approach to flexible protein–ligand docking

paper_content:

The prediction of the complex structure of a small ligand with a protein, the so-called protein–ligand docking problem, is a central part of the rational drug design process. For this purpose, we introduce the docking algorithm PLANTS (Protein–Ligand ANT System), which is based on ant colony optimization, one of the most successful swarm intelligence techniques. We study the effectiveness of PLANTS for several parameter settings and present a direct comparison of PLANTS’s performance to a state-of-the-art program called GOLD, which is based on a genetic algorithm and frequently used in the pharmaceutical industry for this task. Last but not least, we also show that PLANTS can make effective use of protein flexibility giving example results on cross-docking and virtual screening experiments for protein kinase A.
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paper_title: Accelerating Molecular Docking Calculations Using Graphics Processing Units

paper_content:

The generation of molecular conformations and the evaluation of interaction potentials are common tasks in molecular modeling applications, particularly in protein−ligand or protein−protein docking programs. In this work, we present a GPU-accelerated approach capable of speeding up these tasks considerably. For the evaluation of interaction potentials in the context of rigid protein−protein docking, the GPU-accelerated approach reached speedup factors of up to over 50 compared to an optimized CPU-based implementation. Treating the ligand and donor groups in the protein binding site as flexible, speedup factors of up to 16 can be observed in the evaluation of protein−ligand interaction potentials. Additionally, we introduce a parallel version of our protein−ligand docking algorithm PLANTS that can take advantage of this GPU-accelerated scoring function evaluation. We compared the GPU-accelerated parallel version to the same algorithm running on the CPU and also to the highly optimized sequential CPU-based ...
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paper_title: Ultra-fast FFT protein docking on graphics processors

paper_content:

Motivation: Modelling protein–protein interactions (PPIs) is an increasingly important aspect of structural bioinformatics. However, predicting PPIs using in silico docking techniques is computationally very expensive. Developing very fast protein docking tools will be useful for studying large-scale PPI networks, and could contribute to the rational design of new drugs. ::: ::: Results: The Hex spherical polar Fourier protein docking algorithm has been implemented on Nvidia graphics processor units (GPUs). On a GTX 285 GPU, an exhaustive and densely sampled 6D docking search can be calculated in just 15 s using multiple 1D fast Fourier transforms (FFTs). This represents a 45-fold speed-up over the corresponding calculation on a single CPU, being at least two orders of magnitude times faster than a similar CPU calculation using ZDOCK 3.0.1, and estimated to be at least three orders of magnitude faster than the GPU-accelerated version of PIPER on comparable hardware. Hence, for the first time, exhaustive FFT-based protein docking calculations may now be performed in a matter of seconds on a contemporary GPU. Three-dimensional Hex FFT correlations are also accelerated by the GPU, but the speed-up factor of only 2.5 is much less than that obtained with 1D FFTs. Thus, the Hex algorithm appears to be especially well suited to exploit GPUs compared to conventional 3D FFT docking approaches. ::: ::: Availability: http://hex.loria.fr/ and http://hexserver.loria.fr/ ::: ::: Contact: dave.ritchie@loria.fr ::: ::: Supplementary information:Supplementary data are available at Bioinformatics online.
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paper_title: Effective Parallelization of Non-bonded Interactions Kernel for Virtual Screening on GPUs

paper_content:

In this work we discuss the benefits of using massively parallel architectures for the optimization of Virtual Screeningmethods.We empirically demonstrate that GPUs are well suited architecture for the acceleration of non-bonded interaction kernels, obtaining up to a 260 times sustained speedup compared to its sequential counterpart version.
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paper_title: A molecular docking system using CUDA

paper_content:

A Molecular Docking System enables biologists to check whether two molecular models can be combined at a specific position and remain in their stable states by simulation. It can be used in developing new materials and designing new drugs. Since the docking simulation consists of several complicated computations at the level of atoms, it requires high computing capabilities such as super computers and parallel computing systems. We propose a molecular docking system using parallel GPUs in this paper. In our proposed method, a GPU can process an equation as a single logical work unit. The computations can be executed through parallel GPUs in real-time. The proposed system was evaluated in its performance by comparing with conventional CPU-based systems. A series of experiments for measuring performance of the system showed that our system is 33 to 287 times faster than the CPU-based systems.
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Title: Hardware Accelerated Molecular Docking: A Survey

Section 1: Introduction
Description 1: Introduction to the concept of hardware acceleration and its relevance in bioinformatics, specifically molecular docking.

Section 2: Overview of molecular docking
Description 2: Description of the molecular docking process, including various docking techniques and their applications in drug design.

Section 3: FPGA devices
Description 3: General overview of FPGA devices, their architecture, and their application in computing tasks.

Section 4: GPU devices
Description 4: General overview of GPU devices, their architecture, and their application in parallel computing.

Section 5: Molecular docking on FPGA platforms
Description 5: Detailed introduction to the existing FPGA-based docking implementations and their methodologies.

Section 6: Docking with 3D correlation
Description 6: Explanation of the 3D correlation-based FPGA docking implementation.

Section 7: PIPER on FPGA
Description 7: Detailed description of the FPGA-based implementation of the PIPER docking program.

Section 8: AutoDock on FPGA
Description 8: Description of the FPGA-based acceleration of the AutoDock docking software.

Section 9: GPU-based implementations
Description 9: Overview of several GPU-based molecular docking implementations and their comparative advantages.

Section 10: PIPER on GPU
Description 10: Detailed explanation of the GPU-based version of PIPER docking program.

Section 11: A general FFT-based approach
Description 11: Description of a CUDA implementation that applies FFT for performing the correlation-based docking algorithm.

Section 12: AutoDock on GPU
Description 12: Detailed introduction to various approaches to GPU-based acceleration of AutoDock.

Section 13: MolDock on GPU
Description 13: Overview of GPU-based acceleration of MolDock docking software.

Section 14: PLANTS on GPU
Description 14: Description of the GPU-accelerated PLANTS docking software using ant colony optimization.

Section 15: Other approaches
Description 15: Other notable GPU-based docking solutions and their unique methodologies.

Section 16: Conclusion
Description 16: Summary of the surveyed FPGA and GPU-based molecular docking implementations, performance comparisons, and final remarks on their applicability.