hero image
Olexandr Isayev - Carnegie Mellon University. Pittsburgh, PA, US

Olexandr Isayev

Assistant Professor, Chemistry | Carnegie Mellon University


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


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 (7)

Theoretical Chemistry

Computational Chemistry

Machine Learning


Drug Discovery

Computer-aided Molecular Design

Materials Informatics

Media Appearances (5)

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

NVIDIA  online


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.

view more

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

HPC Wire  online


“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.

view more

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

HPC Wire  online


Olexandr Isayev from Carnegie Mellon University received 350,000 node hours for “Interpretable Machine Learning Force Fields for Accurate Chemical Reactive Dynamics.”

view more

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

Enterprise AI  online


“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.”

view more

AI identifies drug candidate in weeks

Chemical & Engineering News  online


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.”

view more






HITS/SIMPLAIX seminar: Olexandr Isayev on neural networks learning computational chemistry Olexandr Isayev - Accelerating Drug Discovery with Machine Learning and AI HITS Colloquium: Olexandr Isayev on Machine Learning in Chemistry Olexandr Isayev, Carnegie Mellon University | Machine Learning Workshop Neural Networks Learning Quantum Chemistry | Olexandr Isayev



Industry Expertise (3)




Accomplishments (5)

Eshelman Institute for Innovation Award (professional)


Chemical Structure Association Trust Award (professional)


NVIDIA GPU Computing Award (professional)


IBM-Löwdin Memorial Fellowship (professional)


ACS Emerging Technology Award (professional)

2017, 2014

Education (2)

Dnepropetrovsk National University: M.S., Chemistry 2002

Jackson State University: Ph.D., Theoretical Chemistry 2008

Articles (5)

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.

view more

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.

view more

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.

view more

Comprehensive exploration of graphically defined reaction spaces

Scientific Data

2023 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.

view more

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

Biophysical Journal

2023 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.

view more