Olexandr Isayev

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Assistant Professor

Carnegie Mellon University

United States

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About

Olexandr Isayev is an Assistant Professor at Carnegie Mellon University in the United States. His research areas involve the intersection of machine learning and computational chemistry, focusing on the design and evaluation of chemometric models and neural network potentials for organic molecules. Recent articles include work on 3D printable elastomers and advancements in modeling crystallizable organic semiconductors using machine learning techniques.

Recent Grants

Grant: Close

D3SC: CDS&E: Collaborative Research: Development and application of accurate, transferable and extensible deep neural network potentials for molecules and reactions

Open Date: 2018-07-15

Close Date: 2021-06-30

Articles (21)

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Assistant Professor

Trinity University

UNITED STATES

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Olexandr Isayev

Research Interests

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Contact this professor

About

Recent Grants

Articles (21)

Design of Tough 3D Printable Elastomers with Human‐in‐the‐Loop Reinforcement Learning

Design of Tough 3D Printable Elastomers with Human‐in‐the‐Loop Reinforcement Learning

All That Glitters Is Not Gold: Importance of Rigorous Evaluation of Proteochemometric Models

ANI-1xBB: An ANI-Based Reactive Potential for Small Organic Molecules

Including Physics-Informed Atomization Constraints in Neural Networks for Reactive Chemistry

AIMNet2: a neural network potential to meet your neutral, charged, organic, and elemental-organic needs

In silico screening of LRRK2 WDR domain inhibitors using deep docking and free energy simulations

Discovery of Crystallizable Organic Semiconductors with Machine Learning

Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential

MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows

ANI/EFP: Modeling Long-Range Interactions in ANI Neural Network with Effective Fragment Potentials

Active Learning Guided Drug Design Lead Optimization Based on Relative Binding Free Energy Modeling

Synergy of semiempirical models and machine learning in computational chemistry

Structure Prediction of Epitaxial Organic Interfaces with Ogre, Demonstrated for Tetracyanoquinodimethane (TCNQ) on Tetrathiafulvalene (TTF)

Generative Models as an Emerging Paradigm in the Chemical Sciences

Machine Learning Interatomic Potentials and Long-Range Physics

Scalable hybrid deep neural networks/polarizable potentials biomolecular simulations including long-range effects

Themed collection on Insightful Machine Learning for Physical Chemistry

Δ2 machine learning for reaction property prediction

Generative and reinforcement learning approaches for the automated de novo design of bioactive compounds

Auto3D: Automatic Generation of the Low-Energy 3D Structures with ANI Neural Network Potentials

Collaborators (10)

Frank Leibfarth

Francesco Gentile

Isaac Tamblyn

Lyudmila V Slipchenko

Maria Kurnikova

Timothy Willson

Adrian Roitberg

Alexander Tropsha

Dylan Anstine

Meredith Borden

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