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.
Biography
Areas of Expertise
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."
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.
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.
ALCC Program Awards Computing Time on ALCF’s Theta Supercomputer to 24 projects
HPC Wire online
2020-08-06
Olexandr Isayev from Carnegie Mellon University received 350,000 node hours for “Interpretable Machine Learning Force Fields for Accurate Chemical Reactive Dynamics.”
Chemical Discovery for Industry, Medical with Carnegie Mellon’s Neural Network Tool
Enterprise AI online
2020-04-21
“Using AI and ML, our team developed a neural network potential tool named ANI that is trained to solve particular quantum mechanical problems,” Isayev said. “One of the goals in creating ANI was to develop a tool that would be able to predict how a molecule will respond in real-world conditions considering the quantum mechanical behavior when it reacts with other chemical systems.”
AI identifies drug candidate in weeks
Chemical & Engineering News online
2019-09-04
Other machine-learning experts are less convinced that Insilico Medicine’s 46-day time line is such an achievement. Traditional drug-discovery techniques might have worked just as quickly, says Olexandr Isayev, a computational chemist at the University of North Carolina at Chapel Hill. The researchers don’t provide a baseline for comparison. Without that, adds Ash Jogalekar, a medicinal chemist at the AI-oriented biotech firm Strateos, “it’s thus impossible to know whether the results attributed to the technique are unique in any way or not.”
Media
Social
Industry Expertise
Accomplishments
ACS Emerging Technology Award
2017, 2014
IBM-Löwdin Memorial Fellowship
2009
NVIDIA GPU Computing Award
2014
Chemical Structure Association Trust Award
2015
Eshelman Institute for Innovation Award
2016
Education
Jackson State University
Ph.D.
Theoretical Chemistry
2008
Dnepropetrovsk National University
M.S.
Chemistry
2002
Articles
High-throughput binding free energy simulations: Applications in drug discovery
Biophysical Journal2023
WDR domain using BFE calculations with thermodynamic integration. LRRK2 is the most commonly mutated gene in familial Parkinson’s Disease, and its WDR domain is an understudied drug target with no known molecular inhibitors. This work was done as a submission to the Critical Assessment of Computational Hit-Finding Experiments (CACHE) Challenge. We also discuss various methods to minimize computational cost while maintaining accuracy and make recommendations for future high-throughput screening campaigns.
Comprehensive exploration of graphically defined reaction spaces
Scientific Data2023
Existing reaction transition state (TS) databases are comparatively small and lack chemical diversity. Here, this data gap has been addressed using the concept of a graphically-defined model reaction to comprehensively characterize a reaction space associated with C, H, O, and N containing molecules with up to 10 heavy (non-hydrogen) atoms. The resulting dataset is composed of 176,992 organic reactions possessing at least one validated TS, activation energy, heat of reaction, reactant and product geometries, frequencies, and atom-mapping.
Active learning guided drug design lead optimization based on relative binding free energy modeling
Journal of Chemical Information and Modeling2023
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.
Machine Learning Interatomic Potentials and Long-Range Physics
The Journal of Physical Chemistry A2023
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.
Generative Models as an Emerging Paradigm in the Chemical Sciences
Journal of the American Chemical Society2023
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.
