Recent Publications

Accurate and Transferable Multitask Prediction of Chemical Properties with an Atoms-in-Molecule Neural Network

Roman Zubatyuk, Justin S. Smith, Jerzy Leszczynski, Olexandr Isayev.  2019In revision

Efficient prediction of structural and electronic properties of hybrid 2D materials using DFT and machine learning

Sherif A. Tawfik, Olexandr Isayev, Catherine Stampfl, Joe Shapter, David A. Winkler, Michael J. Ford.  Adv. Theory Simul., 2019, 2: 1800128

Machine learning for molecular and materials science

K. T. Butler, D. W. Davies, H. Cartwright, O. Isayev*, A. Walsh. Nature, 2018, 559, 547–555.

Transforming Computational Drug Discovery with Machine Learning and AI

J. S. Smith, A. E. Roitberg, O. Isayev*. T. ACS Med. Chem. Lett. 2018. 9, 1065–1069

Outsmarting Quantum Chemistry Through Transfer Learning

J. S. Smith, B. T. Nebgen, R. Zubatyuk, N. Lubbers, C. Devereux, K. Barros, S. Tretiak, O. Isayev*, A. Roitberg. 2018 In revision

Discovering a Transferable Charge Assignment Model Using Machine Learning

Andrew E. Sifain, Nicholas Lubbers, Benjamin T. Nebgen, Justin S. Smith, Andrey Y. Lokhov, Olexandr Isayev, Adrian E. Roitberg, Kipton Barros, and Sergei Tretiak. J. Phys. Chem. Lett., 2018, 9 (16), pp 4495–4501.

Deep Reinforcement Learning for de-novo Drug Design

M. Popova, O. Isayev*, A. Tropsha. Science Advances, 2018, 4 (7) ,eaap7885.

AFLOW-ML: A RESTful API for machine-learning predictions of materials properties

Eric Gossett, Cormac Toher, Corey Oses, Olexandr Isayev, Fleur Legrain, Frisco Rosea, Eva Zurek, Jesús Carrete, Natalio Mingo, Alexander Tropsha, Stefano Curtarolo. Computational Materials Science, 152, 2018, 134-145.

Transferable Molecular Charge Assignment Using Deep Neural Networks

B. Nebgen, N. Lubbers, J. S Smith, A. Sifain, A. Lokhov, O. Isayev, A. Roitberg, K. Barros, S. Tretiak. J. Chem. Theory Comput., 2018, 14, 4687–4698

Less is more: Sampling chemical space with active learning

J. S. Smith, B. Nebgen, N. Lubbers, O. Isayev*, A. E. Roitberg. Journal of Chemical Physics 2018, 148, 241733.

ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost

J. S. Smith, O. Isayev*, A. E. Roitberg. Chem. Sci., 2017, 8, 3192-3203.

Universal Fragment Descriptors for Predicting Electronic Properties of Inorganic Crystals

O. Isayev*, C. Oses, C. Toher, E. Gossett, S. Curtarolo, A. Tropsha. Nature Commun. 2017, 8, 15679

ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules

J. S. Smith, O. Isayev*, A. E. Roitberg. Scientific Data, 2017, 4, Article number: 170193.

Material Informatics Driven Design and Experimental Validation of Lead Titanate as an Aqueous Solar Photocathode

T. Moot, O. Isayev, R. W. Call, S. M. McCullough, M. Zemaitis, R. Lopez, J. F. Cahoon, A. Tropsha.  Materials Discovery. 2017, 6, 9-16.

QSAR modeling of Tox21 challenge stress response and nuclear receptor signaling toxicity assays

S. J. Capuzzi, R. Politi, O. Isayev, S. Farag, A. Tropsha. Frontiers in Environmental Science, 2016, 4, 3.

Materials Cartography: Representing and Mining Materials Space Using Structural and Electronic Fingerprints

O. Isayev, D. Fourches, E.N. Muratov, C. Oses, K.M. Rasch, A. Tropsha, and S. Curtarolo. Chem. Mater., 2015, 27, 735-742.

Are the reduction and oxidation properties of nitrocompounds dissolved in water different from those adsorbed on a silica surface?

L. K. Sviatenko, O. Isayev, L. Gorb, F. C. Hill, D. Leszczynska, J. Leszczynski. J. Comp. Chem. 2015, 36 1029-1036. (Cover Article)

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