Olexandr Isayev

Assistant Professor, Chemistry Carnegie Mellon University

  • Pittsburgh PA

Olexandr Isayev's research focuses on solving fundamental chemical problems with machine learning, molecular modeling and quantum mechanics.

Contact

Carnegie Mellon University

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Biography

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.

Areas of Expertise

Theoretical Chemistry
Computational Chemistry
Machine Learning
Cheminformatics
Drug Discovery
Computer-aided Molecular Design
Materials Informatics

Media Appearances

Machine learning approach discovers crystallizable organic semiconductors

MSN  online

2024-11-25

In collaboration with scientists at Princeton University, Carnegie Mellon University researchers Filipp Gusev and Olexandr Isayev, the Carl and Amy Jones Professor of Interdisciplinary Science, devised a way to use machine learning (ML) to rapidly identify potential COS materials.

"Rather than going into a lab and doing thousands of experiments to search for COS materials, we trained ML models that would guide us on what molecules to pick," Isayev said. "Essentially, you start with a haystack and end up with a few needles you were looking for."

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

NVIDIA  online

2022-06-22

It may seem intuitive that AI and deep learning can speed up workflows — including novel drug discovery, a typically years-long and several-billion-dollar endeavor.

But professors Artem Cherkasov and Olexandr Isayev were surprised to find that no recent academic papers provided a comprehensive, global research review of how deep learning and GPU-accelerated computing impact drug discovery.

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

HPC Wire  online

2022-01-06

“Previous efforts in materials discovery have relied on either luck or human intuition, which both suffer from inherent biases and limitations in knowledge,” said Olexandr Isayev at CMU.

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Media

Social

Industry Expertise

Education/Learning
Pharmaceuticals
Chemicals

Accomplishments

Eshelman Institute for Innovation Award

2016

Chemical Structure Association Trust Award

2015

NVIDIA GPU Computing Award

2014

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Education

Dnepropetrovsk National University

M.S.

Chemistry

2002

Jackson State University

Ph.D.

Theoretical Chemistry

2008

Articles

Generative Models as an Emerging Paradigm in the Chemical Sciences

Journal of the American Chemical Society

2023

raditional computational approaches to design chemical species are limited by the need to compute properties for a vast number of candidates, e.g., by discriminative modeling. Therefore, inverse design methods aim to start from the desired property and optimize a corresponding chemical structure. From a machine learning viewpoint, the inverse design problem can be addressed through so-called generative modeling. Mathematically, discriminative models are defined by learning the probability distribution function of properties given the molecular or material structure.

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Machine Learning Interatomic Potentials and Long-Range Physics

The Journal of Physical Chemistry A

2023

Advances in machine learned interatomic potentials (MLIPs), such as those using neural networks, have resulted in short-range models that can infer interaction energies with near ab initio accuracy and orders of magnitude reduced computational cost. For many atom systems, including macromolecules, biomolecules, and condensed matter, model accuracy can become reliant on the description of short- and long-range physical interactions. The latter terms can be difficult to incorporate into an MLIP framework. Recent research has produced numerous models with considerations for nonlocal electrostatic and dispersion interactions, leading to a large range of applications that can be addressed using MLIPs.

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

Journal of Chemical Information and Modeling

2023

In silico identification of potent protein inhibitors commonly requires prediction of a ligand binding free energy (BFE). Thermodynamics integration (TI) based on molecular dynamics (MD) simulations is a BFE calculation method capable of acquiring accurate BFE, but it is computationally expensive and time-consuming. In this work, we have developed an efficient automated workflow for identifying compounds with the lowest BFE among thousands of congeneric ligands, which requires only hundreds of TI calculations. Automated machine learning (AutoML) orchestrated by active learning (AL) in an AL–AutoML workflow allows unbiased and efficient search for a small set of best-performing molecules.

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