Recent Publications

TorchANI: A Free and Open Source PyTorch Based Deep Learning Implementation of the ANI Neural Network Potentials

Xiang Gao, Farhad Ramezanghorbani, Olexandr Isayev, Justin S. Smith, and Adrian E. Roitberg. J. Chem. Inf. Model. 2020, In press 

Extending the applicability of the ANI deep learning molecular potential to Sulfur and Halogens

Christian Devereux, Justin S. Smith, Kate K Davis, Kipton Barros, Roman Zubatyuk, Olexandr Isayev, and Adrian E. Roitberg. J. Chem. Theory Comput. 2020, In press 

The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules

3. J. S. Smith, R. Zubatyuk, B. T. Nebgen, N. Lubbers, Kipton Barros, A. Roitberg, O. Isayev, S. Tretiak  Scientific Data 2020, 7, 134.

Crowdsourced mapping extends the target space of kinase inhibitors

Anna Cichonska, Balaguru Ravikumar, Robert J Allaway, Sungjoon Park, Fangping Wan, Olexandr Isayev, Shuya Li, Michael Mason, Andrew Lamb, Ziaurrehman Tanoli, Minji Jeon, Sunkyu Kim, Mariya Popova, Stephen Capuzzi, Jianyang Zeng, Kristen Dang, Gregory Koytiger, Jaewoo Kang, Carrow I. Wells, Timothy M. Willson, The IDG-DREAM Drug-Kinase Binding Prediction Challenge Consortium, Tudor I. Oprea, Avner Schlessinger, David H. Drewry, Gustavo Stolovitzky, Krister Wennerberg, Justin Guinney, Tero Aittokallio
bioRxiv 2020

Predicting Thermal Properties of Crystals Using Machine Learning

Sherif Abdulkader Tawfik Olexandr Isayev Michelle J. S. Spencer David A. Winkler.  Adv. Theory Simul., 2020 3: 1900208 

MolecularRNN: Generating realistic molecular graphs with optimized properties

Mariya Popova, Mykhailo Shvets, Junier Oliva, Olexandr Isayev. Preprint 2019

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

Roman Zubatyuk, Justin S. Smith, Jerzy Leszczynski, Olexandr Isayev. Science Advances 2019. 5, aav6490

Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning

J. S. Smith, B. T. Nebgen, R. Zubatyuk, N. Lubbers, C. Devereux, K. Barros, S. Tretiak, O. Isayev*, A. Roitberg. Nature Commun. 2019, 10, 2903.

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

Inter-Modular Linkers play a crucial role in governing the biosynthesis of non-ribosomal peptides

Sherif Farag, Rachel M Bleich, Elizabeth A Shank, Olexandr Isayev, Albert A Bowers, Alexander Tropsha. Bioinformatics, 2019, 35,  3584–3591.

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

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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