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Eshelman Institute for Innovation Award

Chemical Structure Association Trust Award

NVIDIA GPU Computing Award

IBM-Löwdin Memorial Fellowship

ACS Emerging Technology Award

Generative Models as an Emerging Paradigm in the Chemical Sciences

Machine Learning Interatomic Potentials and Long-Range Physics

Active learning guided drug design lead optimization based on relative binding free energy modeling

Comprehensive exploration of graphically defined reaction spaces

High-throughput binding free energy simulations: Applications in drug discovery

Working to connect artificial intelligence with chemical sciences, Olexandr Isayev's research focuses on solving fundamental chemical problems with machine learning, molecular modeling and quantum mechanics. his lab works at the interface of theoretical chemistry, pharmaceutical sciences and computer science, working in such areas as theoretical and computational chemistry, machine learning, cheminformatics, drug discovery, computer-aided molecular design and materials informatics.

Theoretical Chemistry

Computational Chemistry

Machine Learning

Cheminformatics

Drug Discovery

Computer-aided Molecular Design

Materials Informatics

Carnegie Mellon University Profile 

Google Scholar Citations

Machine learning approach discovers crystallizable organic semiconductors

Artem Cherkasov and Olexandr Isayev on Democratizing Drug Discovery With NVIDIA GPUs

AI-Based ‘Artificial Chemist’ Directs Automated Lab to Create Better MRI Contrast Agents

ALCC Program Awards Computing Time on ALCF’s Theta Supercomputer to 24 projects

Chemical Discovery for Industry, Medical with Carnegie Mellon’s Neural Network Tool

AI identifies drug candidate in weeks

Assistant Professor, Chemistry

Neural networks learning computational chemistry
Olexandr Isayev (Carnegie Mellon University, Pittsburgh PA).
https://www.cmu.edu/chemistry/people/faculty/isayev.html 

Scientific seminar of HITS & SIMPLAIX
26 September 2022, HITS

HITS/SIMPLAIX seminar: Olexandr Isayev on neural networks learning computational chemistry

Presented on 2/4/2021

Deep learning is revolutionizing many areas of science and technology, particularly in natural language processing, speech recognition, and computer vision. In this talk, we will provide an overview of the latest developments of machine learning and AI methods and their application to the problem of drug discovery and development at Isayev’s Lab at CMU. We identify several areas where existing methods have the potential to accelerate pharmaceutical research and disrupt more traditional approaches. First, we will present a deep learning model that approximates the solution of the Schrodinger equation. Focusing on parametrization for drug-like organic molecules and proteins, we have developed a single ‘universal’ model that is highly accurate compared to reference quantum mechanical calculations at speeds 10^6 faster. Second, we proposed a novel computational method for the de-novo design of molecular compounds with desired biological properties. The general workflow of the proposed method integrates two deep neural networks – generative and predictive – that are initially trained separately but then trained jointly to generate novel chemical structures with desired properties.  In the proof-of-concept study, we have employed this integrative strategy to design chemical libraries biased toward compounds with either maximal, minimal, or specific ranges of physical properties (like solubility and hydrophobicity) as well as to develop novel kinase inhibitors of JAK2, EGFR, CDK1, and TBK1. This new approach can find general use for generating targeted chemical libraries optimized for a single desired property or polypharmacology.

Bio:
Olexandr Isayev is an Assistant Professor at the Department of Chemistry at Carnegie Mellon University. In 2008, Olexandr received his Ph.D. in computational chemistry. He was Postdoctoral Research Fellow at the Case Western Reserve University and a scientist at the government research lab. During 2016-2019 he was a faculty at UNC Eshelman School of Pharmacy, the University of North Carolina at Chapel Hill. Olexandr received the “Emerging Technology Award” from the American Chemical Society (ACS) and the GPU computing award from NVIDIA. The research in his lab focuses on connecting artificial intelligence (AI) with chemical sciences. 

Links:
https://groups.chem.cmu.edu/isayev
https://docs.google.com/document/d/1GW3-KDzEVdui9RHBqfGJku9gE50UCLCmU7Gv4G_PbiA/edit

Olexandr Isayev - Accelerating Drug Discovery with Machine Learning and AI

Accelerating Design of Organic Materials with Machine Learning and AI.

HITS Colloquium by Olexandr Isayev, Assistant professor, Carnegie Mellon University, USA.  

Hybrid Talk, Studio Villa Bosch, Heidelberg, Germany, 13 June 2023
More Info: https://www.h-its.org/event/colloquium-olexandr-isayev/

HITS Colloquium: Olexandr Isayev on Machine Learning in Chemistry

Speaker: Olexandr Isayev, Carnegie Mellon University
Talk: Neural Networks Learning Computational Chemistry

Workshop: Foundations of Machine Learning and Its Applications for Scientific Discovery in Physical and Biological Systems
June 2022 | Washington, DC Metro

More Information: http://wp.me/P3qDa4-QP

Hosted by Rice University
Funded by NSF

Olexandr Isayev, Carnegie Mellon University | Machine Learning Workshop

TÜBİTAK TBAE Chemical Sciences Seminar Series

Neural Networks Learning Quantum Chemistry

Speaker: Olexandr Isayev (Carnegie Mellon University, USA)


ABSTRACT: Historically, computational chemistry has been unable to overcome the orthogonal requirements of speed and accuracy. If you want an accurate calculation, be ready to pay the price in computer time. In the pursuit of a compromise between speed and accuracy, many approximations were developed that expedite ab-initio methods with some loss of accuracy. Popular strategies include the partition of the system of interest into fragments, linear scaling, semi-empirical methods, or the construction of empirical potentials that have been parametrized to reproduce experimental or accurate ab-initio data. Artificial intelligence (AI) methods that focus on the use of large and diverse data sets in training new atomistic potentials have consistently proven to be universally applicable to systems containing the atomic species in the training set. In this talk, we will present a fully transferable deep learning potential that is applicable to complex and diverse molecular systems well beyond the training data. Focusing on parametrization for organic molecules (with CHNOSFCl atoms so far), we have developed a universal neural network potential which is highly accurate compared to reference QM calculations at speeds 10^7 faster. The potential is shown to accurately represent the underlying physical chemistry of molecules through various test cases including chemical reactions (both thermodynamics and kinetics), thermochemistry, structural optimization, and molecular dynamics simulations. The results presented in this talk will provide evidence of the broad applicability of deep learning to various chemistry problems involving organic molecules.

Neural Networks Learning Quantum Chemistry | Olexandr Isayev

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