aayush gupta , phd
AI Research Scientist at Exscientia
computational chemist & biophysicist | drug discovery | molecular modeling | ai/ml researcher | entrepreneur
innovation-driven computational chemist . research interest involve integration machine learning next- generation quantum classical molecular modeling . versatile skill computer programming , data science , building linux server gpus . strong experience developing computational workflow solving electronic structure protein-drug modeling . entrepreneurial plus independent research skill framing new idea , building prototype documenting peer-reviewed journal ( track record first-author publication ) . passionate learning implementing novel challenging technique advancing science .
Exscientia
https://www.linkedin.com/company/ex-scientia
Inventing new approaches to AI based drug discovery.
Miami, Florida, United States
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AI Research Scientist
University of Illinois Chicago
https://www.linkedin.com/company/thisisuic
o Implemented an AI based generative deep network to guide conformational sampling of intrinsically disordered proteins (IDPs). Proposed a protocol to accelerate (learn and generate) MD simulations.
o Designed an efficient workflow for drug discovery by integrating a hybrid neural-network (pseudo-quantum) and classical forcefields based molecular dynamics simulations against COVID-19 main protease (MPRO).
o Developed a ML-enabled pipeline for large-scale virtual drug screening using clustering and deep learning in combination with physics-based approaches against RPN11 - a drug target for breast cancer.
o Other areas: Modeling electron transport in peptide helices subject to chirality induced spin selectivity (CISS) effect and its application in protein-protein association.

o Performed quantum and classical simulations on a variety of biomolecular systems. Experienced with handling large datasets. Wrote several customized Python and Bash scripts to handle research data.
o Built and administering server-client protocol for our research lab using LDAP. Troubleshooting software-related issues and benchmarking computational chemistry packages on our in-house (lab) GPU cluster.
Chicago, Illinois
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PhD Research Assistant
University of Illinois Chicago
https://www.linkedin.com/company/thisisuic
Demonstrated teaching capabilities in lab as well as classroom. I have successfully completed TA duties in the following courseworks:
•	Physical Chemistry for Biochemists; Thermodynamics : Leading discussion in-person classes and problem solving sessions with students in their senior year.
•	Physical Chemistry I & II; Quantum Chemistry, Statistical Thermodynamics, Spectroscopy, Computational Chemistry : Leading discussion, grading and problem solving for junior year students.
•	General Chemistry I & II; Undergraduate Chemistry : Instructed students to perform experiments in the wet lab. Leading discussions and problem solving session.
Chicago, Illinois
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Graduate Teaching Assistant
Schrödinger
https://www.linkedin.com/company/schr-dinger
o Evaluated performance of deep neural network potentials/ force-fields (ANI-1) to achieve DFT accuracy at force-field speed (100x) for small molecule crystal polymorph prediction. Reported 98% DFT-ANI1 correlation in prediction.
o Analyzed potential energy surfaces of 100 different crystal structures (over 500 polymorphs) using DFT/ANI-1 potentials and identified their experimental stable forms (from exhaustive literature search).
Greater New York City Area
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Research Internship
Moscow Institute of Physics and Technology (State University) - MIPT, Phystech
https://www.linkedin.com/company/moscow-institute-of-physics-and-technology-state-university-
o Used evolutionary algorithm (USPEX) to predict the stable and metastable structures of europium nitride (with the chemical composition only).
o Reported the most crystal stable structure in reference to the experimental literature.
Moscow, Russian Federation
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Research
Indian Institute of Science (IISc)
https://www.linkedin.com/company/indian-institute-of-science
o Worked with Solid State and Structural Chemistry Unit at IISc
o Performed nano-scale molecular dynamics simulations of Zeolite MOF using DLPOLY package.
o Analyzed different warfare agents (Xylene, Benzene etc) adsorbed on the Zeolite surface.
Bengaluru Area, India
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Research Internship
Bhabha Atomic Research Centre
https://www.linkedin.com/company/bhabha-atomic-research-centre
o Worked at Theoretical Chemistry Section at BARC
o Carried out periodic DFT on novel porous carbon nitride (C3N4) as a catalyst for photooxidation of water.
o Investigated 4e- and 2e- water splitting reactions (DFT calculations using GAMESS package).
Mumbai Area, India
Research
Council of Scientific and Industrial Research
https://www.linkedin.com/company/council-of-scientific-and-industrial-research_2
o Worked at one of the CSIR lab - National Institute of Interdisciplinary Science at Technology at Thiruvananthapuram, Kerala
o Modeled reaction isomerization path (cyclopropene to allene) using first principle methods.
o Proposed a new reaction pathway with smaller activation energy (performed transition state DFT calculations).
Trivandrum
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Indian Academy of Science Fellow
Crystal Chemicals
https://www.linkedin.com/company/crystal-chemicals
In Plant Trainee
Doctor of Philosophy (PhD)
Computational Chemistry
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University of Illinois at Chicago
https://www.linkedin.com/school/thisisuic/
Master of Science - MS
Chemistry
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University of Illinois Chicago
https://www.linkedin.com/school/thisisuic/
Bachelor of Chemical Technology
o This work was first of its kind to explore the mysteries of "Hexasilabenzenoids" using quantum mechanical simulations. 
o We unfolded a novel property of this material which can have potential impact on Silicene based studies. 
o Find link to the published paper at the bottom !
Chemical Technology
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Institute Of Chemical Technology
https://www.linkedin.com/school/institute-of-chemical-technology/
Biophysical Society
Graduate Member
IASC-INSA-NASI, India
Summer Research Fellow 2014
Aayush Gupta, Souvik Dey, Huan-Xiang Zhou

Artificial intelligence recently achieved the breakthrough of predicting the three-dimensional structures of proteins. The next frontier is presented by intrinsically disordered proteins (IDPs), which, representing 30% to 50% of proteomes, readily access vast conformational space. Molecular dynamics (MD) simulations are promising in sampling IDP conformations, but only at extremely high computational cost. Here, we developed generative autoencoders that learn from short MD simulations and generate full conformational ensembles. An encoder represents IDP conformations as vectors in a reduced-dimensional latent space. The mean vector and covariance matrix of the training dataset are calculated to define a multivariate Gaussian distribution, from which vectors are sampled and fed to a decoder to generate new conformations. The ensembles of generated conformations cover those sampled by long MD simulations and are validated by small-angle X-ray scattering profile and NMR chemical shifts. This work illustrates the vast potential of artificial intelligence in conformational mining of IDPs.
Artificial Intelligence Guided Conformational Mining of Intrinsically Disordered Proteins
BioRxiv
https://www.biorxiv.org/content/10.1101/2021.11.21.469457v1
Aayush Gupta, Huan-Xiang Zhou

Virtual screening is receiving renewed attention in drug discovery, but progress is hampered by challenges on two fronts: handling the ever-increasing sizes of libraries of drug-like compounds and separating true positives from false positives. Here, we developed a machine learning-enabled pipeline for large-scale virtual screening that promises breakthroughs on both fronts. By clustering compounds according to molecular properties and limited docking against a drug target, the full library was trimmed by 10-fold; the remaining compounds were then screened individually by docking; and finally, a dense neural network was trained to classify the hits into true and false positives. As illustration, we screened for inhibitors against RPN11, the deubiquitinase subunit of the proteasome, and a drug target for breast cancer.
Machine Learning-Enabled Pipeline for Large-Scale Virtual Drug Screening
Journal of Chemical Information and Modeling
https://pubs.acs.org/doi/10.1021/acs.jcim.1c00710
Aayush Gupta, Huan-Xiang Zhou

The current COVID-19 pandemic caused by a novel coronavirus SARS-CoV-2 urgently calls for a working therapeutic. Here, we report a computation-based workflow for efficiently selecting a subset of FDA-approved drugs that can potentially bind to the SARS-CoV-2 main protease MPRO. The workflow started with docking (using Autodock Vina) each of 1615 FDA-approved drugs to the MPRO active site. This step selected 62 candidates with docking energies lower than −8.5 kcal/mol. Then, the 62 docked protein–drug complexes were subjected to 100 ns of molecular dynamics (MD) simulations in a molecular mechanics (MM) force field (CHARMM36). This step reduced the candidate pool to 26, based on the root-mean-square-deviations (RMSDs) of the drug molecules in the trajectories. Finally, we modeled the 26 drug molecules by a pseudoquantum mechanical (ANI) force field and ran 5 ns hybrid ANI/MM MD simulations of the 26 protein–drug complexes. ANI was trained by neural network models on quantum mechanical density functional theory (wB97X/6-31G(d)) data points. An RMSD cutoff winnowed down the pool to 12, and free energy analysis (MM/PBSA) produced the final selection of 9 drugs: dihydroergotamine, midostaurin, ziprasidone, etoposide, apixaban, fluorescein, tadalafil, rolapitant, and palbociclib. Of these, three are found to be active in literature reports of experimental studies. To provide physical insight into their mechanism of action, the interactions of the drug molecules with the protein are presented as 2D-interaction maps. These findings and mappings of drug–protein interactions may be potentially used to guide rational drug discovery against COVID-19.
Profiling SARS-CoV-2 Main Protease (MPRO) Binding to Repurposed Drugs Using Molecular Dynamics Simulations in Classical and Neural Network-Trained Force Fields
ACS Combinatorial Science
https://pubs.acs.org/doi/abs/10.1021/acscombsci.0c00140
The electronic structure and properties of linearly fused polycyclic hexasilabenzenoids (LF-PCHSBs) (an all silicon – analogue of polyaromatic hydrocarbons) is studied using Density Functional Theoretical (DFT) calculations at B3LYP/6-31G(d,p) level. The structure of LF-PCHSBs display chair like puckering with the formation of ordered pair of ripples. On increasing the number of rings, the oligomers exhibits ∼13° bending. The analysis of orbitals, reorganizational energy () and electronic distribution properties demonstrated an aberrant behaviour with increasing length of PCHSB units. Lower the value of  and uniform electronic distribution along with induced bending of the molecular system, more efficiently these oligomers exhibit the optoelectronics candidacy. The  also helps to authorize the suitability of material for optoelectronic applications and thus assists in designing innovative molecules.
Unexpected Bending in Monolayered Silicene: A Density Functional Theoretical Approach
Journal of Computational and Theoretical Chemistry; Elsevier
http://www.sciencedirect.com/science/article/pii/S2210271X16304492
The Chancellor’s Student Service Award (CSSA) honors students who have made an outstanding contribution to the University through campus and community service.
University of Illinois at Chicago
Chancellor’s Student Service Award
Graduate College
Students Presenter Award
Ministry of Education and Science, Russian Federation
Project 5-100 - Scholarship
Data Science (CME594), Python for Biochemist (CHEM559), Software Design (CS342)
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Quantum Mechanics (CHEM542), Advance Biochemistry (CHEM551), Advanced Inorganic Chemistry (CHEM519), Biophysical Methods (CHEM557), Group Theory and Molecular Spectroscopy (CHEM543),
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1. Photoinduced charge transfer between Boron-12 cluster and styrene.
2. First principle study of electronic structure of metallic organic frameworks (MOFs).
3. Robust CO2 reduction in ionic liquid.
Quantum Mechanical Modelling
1. Prediction of Europium Nitride crystal using USPEX-VASP.
2. Molecular dynamics of Zeolite MOF for adsorption of warfare gases.
3. Photo oxidation of water on a porous c3n4 periodic
4. DFT study of isomerisation of cyclopropene to allene.
5. Particle in a box/ring problem to understand color of organic dye.

Undergraduate Thesis Projects:
1. DFT study on Dehydration and Depolymerization of Cellulose & Fluorescent dyeing of fabrics.
2. Computer aided designing of quaternary ammonium compound.
Internship Projects (Undergraduate)
Science and Technology
Biophysical Society
https://www.linkedin.com/company/biophysical-society
o  https://biophysicschicago.webnode.com/about-us/
o  Organized this networking event focusing on the participation of biophysicists (primarily graduate students and post docs) from the five universities in Chicago. 
o  Full day event provided an opportunity to connect and bond between people in the field of biophysics as well as for potential biophysics candidates to explore the field for cool science!
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Organizer - Biophysics Networking Event at Chicago
Science and Technology
Northwestern University
https://www.linkedin.com/company/northwestern-university
o  https://scienceclub.northwestern.edu/
o  Mentored kids (5th - 7th grades - future scientists) in designing science experiments. 
o  Goal was to make science 'fun and interesting' for kids so that they can opt science in their future.
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Mentor at Science Club
Science and Technology
American Chemical Society
https://www.linkedin.com/company/american-chemical-society
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Panelist at Graduate School Reality Check & Science Coach
Indian National Science Academy - INSA
Theoretical Chemistry Workshop
https://www.linkedin.com/in/aasem-awwad-a4385982
Chicago, IL
Aasem Awwad
Quality Control Chemist at Naturally Vitamins, Marlyn Nutraceuticals
https://in.linkedin.com/in/nilam-pachupate-183ba862
India
Nilam Pachupate
Content Marketing at Thermo Fisher Scientific
https://www.linkedin.com/in/imran-imam-ab7787142
Lombard, IL
Imran Imam
QA Consultant at HCSC Insurance Services Company
https://www.linkedin.com/in/curtis-howard-32037ba1
Greater Chicago Area
Curtis Howard
Branch Manager at Enterprise Rent-A-Car
https://www.linkedin.com/in/michelle-buhrmaster
Western Springs, IL
Michelle Buhrmaster
Management Trainee at Enterprise
https://www.linkedin.com/in/sara-caligiuri-54309b137
Wheaton, IL
Sara Caligiuri
Business Administrator at INNOTEK MEDICAL PRODUCTS, INC
https://www.linkedin.com/in/dwpolhill
Greater Chicago Area
Dan Polhill
Controller at Walton Street Capital
https://www.linkedin.com/in/justin-shelton-b7a03886
Greater Chicago Area
Justin Shelton
Assistant Controller at Alter Domus
https://www.linkedin.com/in/nazia-ali-34b132214
Hoffman Estates, IL
Nazia Ali
Jr. Recruiter at Mirzatech Consulting Inc.
https://www.linkedin.com/in/juan-duarte-81775569
Melrose Park, IL
Juan Duarte
Blocked Account Contracts Documentation Associate at JPMorgan Chase & Co.
https://www.linkedin.com/in/mrinal-shekhar-ab7702b0
Waltham, MA
Mrinal Shekhar
Group Leader, CADD, Research Scientist 1 at Broad Institute of MIT and Harvard
https://www.linkedin.com/in/jason-berg-17658b7
Chicago, IL
Jason Berg
Controller at Benford Capital Partners
https://www.linkedin.com/in/jennifer-labella-693732bb
United States
Jennifer LaBella
Administrator at Legacy Healthcare LLC
https://www.linkedin.com/in/aaronmiller43
Greater Chicago Area
Aaron R. Miller, MS
Healthcare Leader and Administrator
https://www.linkedin.com/in/tate-knudsen-b46886127
Chicago, IL
Tate Knudsen
Training Specialist at Chicago ABA Therapy
https://www.linkedin.com/in/kyle-swanson-bb533043
Greater Chicago Area
Kyle Swanson
Logistics Coordinator at Echo Global Logistics
https://www.linkedin.com/in/jshounak
Los Angeles, CA
Shounak Joshi
PhD student, USC
https://nl.linkedin.com/in/ayonborthakur
Rotterdam and The Hague
Ayon Borthakur, PhD
Practical Machine Learning | Cornell University | IIT  | IISc
Liked by Aayush Gupta, PhD
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A research collaboration of the University of Oxford, CCDC - The Cambridge Crystallographic Data Centre and Exscientia on "hotspot mapping" to…
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Fall in love with problems, tackle the hardest part first, and grow from a culture rooted in experimentation and collaboration. Former Xer and…
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Why Verily, why now? Our senior leaders came to Verily for different reasons, but all with the same goal – to make healthcare work better. Listen to…
Quantum Chemistry on GPU
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