Publications

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

DOI

ChemRxiv

Code

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

DOI

ChemRxiv

Code

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.

DOI

ChemRxiv

Code

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

bioRxiv

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

DOI

2019

MolecularRNN: Generating realistic molecular graphs with optimized properties

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

ArXiv

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

DOI

ChemRxiv

Code

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.

DOI

ChemRxiv

Code

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

DOI

ChemRxiv

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.

DOI

Web App

2018

Machine learning for molecular and materials science

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

DOI

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

DOI

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.

DOI

ChemRxiv

Deep Reinforcement Learning for de-novo Drug Design

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

DOI

ArXiv

Code

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.

DOI

ArXiv

Web App

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

DOI

ArXiv

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.

DOI

ArXiv

Data

2017

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

DOI

ArXiv

Data

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.

DOI

2016

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.

DOI

Data

2015

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.

DOI

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)

DOI

Publications

Recent Publications

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

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

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

Crowdsourced mapping extends the target space of kinase inhibitors

Predicting Thermal Properties of Crystals Using Machine Learning

MolecularRNN: Generating realistic molecular graphs with optimized properties

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

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

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

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

Machine learning for molecular and materials science

Transforming Computational Drug Discovery with Machine Learning and AI

Discovering a Transferable Charge Assignment Model Using Machine Learning

Deep Reinforcement Learning for de-novo Drug Design

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

Transferable Molecular Charge Assignment Using Deep Neural Networks

Less is more: Sampling chemical space with active learning

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

Universal Fragment Descriptors for Predicting Electronic Properties of Inorganic Crystals

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

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

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

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

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

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