Olexandr Isayev
Olexandr Isayev
Associate Professor, Carnegie Mellon University
Verified email at - Homepage
Cited by
Cited by
Machine learning for molecular and materials science
KT Butler, DW Davies, H Cartwright, O Isayev, A Walsh
Nature 559 (7715), 547-555, 2018
ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
JS Smith, O Isayev, AE Roitberg
Chemical science 8 (4), 3192-3203, 2017
Deep reinforcement learning for de novo drug design
M Popova, O Isayev, A Tropsha
Science advances 4 (7), eaap7885, 2018
Less is more: Sampling chemical space with active learning
JS Smith, B Nebgen, N Lubbers, O Isayev, AE Roitberg
The Journal of chemical physics 148 (24), 2018
QSAR without borders
EN Muratov, J Bajorath, RP Sheridan, IV Tetko, D Filimonov, V Poroikov, ...
Chemical Society Reviews 49 (11), 3525-3564, 2020
Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning
JS Smith, BT Nebgen, R Zubatyuk, N Lubbers, C Devereux, K Barros, ...
Nature communications 10 (1), 2903, 2019
Universal fragment descriptors for predicting properties of inorganic crystals
O Isayev, C Oses, C Toher, E Gossett, S Curtarolo, A Tropsha
Nature communications 8 (1), 15679, 2017
Best practices in machine learning for chemistry
N Artrith, KT Butler, FX Coudert, S Han, O Isayev, A Jain, A Walsh
Nature chemistry 13 (6), 505-508, 2021
Materials cartography: representing and mining materials space using structural and electronic fingerprints
O Isayev, D Fourches, EN Muratov, C Oses, K Rasch, A Tropsha, ...
Chemistry of Materials 27 (3), 735-743, 2015
ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules
JS Smith, O Isayev, AE Roitberg
Scientific data 4 (1), 1-8, 2017
Extending the applicability of the ANI deep learning molecular potential to sulfur and halogens
C Devereux, JS Smith, KK Huddleston, K Barros, R Zubatyuk, O Isayev, ...
Journal of Chemical Theory and Computation 16 (7), 4192-4202, 2020
TorchANI: A free and open source PyTorch-based deep learning implementation of the ANI neural network potentials
X Gao, F Ramezanghorbani, O Isayev, JS Smith, AE Roitberg
Journal of chemical information and modeling 60 (7), 3408-3415, 2020
Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network
R Zubatyuk, JS Smith, J Leszczynski, O Isayev
Science advances 5 (8), eaav6490, 2019
The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules
JS Smith, R Zubatyuk, B Nebgen, N Lubbers, K Barros, AE Roitberg, ...
Scientific data 7 (1), 134, 2020
A critical overview of computational approaches employed for COVID-19 drug discovery
EN Muratov, R Amaro, CH Andrade, N Brown, S Ekins, D Fourches, ...
Chemical Society Reviews 50 (16), 9121-9151, 2021
MolecularRNN: Generating realistic molecular graphs with optimized properties
M Popova, M Shvets, J Oliva, O Isayev
arXiv preprint arXiv:1905.13372, 2019
The transformational role of GPU computing and deep learning in drug discovery
M Pandey, M Fernandez, F Gentile, O Isayev, A Tropsha, AC Stern, ...
Nature Machine Intelligence 4 (3), 211-221, 2022
Ab initio molecular dynamics study on the initial chemical events in nitramines: thermal decomposition of CL-20
O Isayev, L Gorb, M Qasim, J Leszczynski
The Journal of Physical Chemistry B 112 (35), 11005-11013, 2008
Discovering a transferable charge assignment model using machine learning
AE Sifain, N Lubbers, BT Nebgen, JS Smith, AY Lokhov, O Isayev, ...
The journal of physical chemistry letters 9 (16), 4495-4501, 2018
Development of multimodal machine learning potentials: toward a physics-aware artificial intelligence
T Zubatiuk, O Isayev
Accounts of Chemical Research 54 (7), 1575-1585, 2021
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