Publications

AIMNet2-rxn: A Machine Learned Potential for Generalized Reaction Modeling on a Millions-of-Pathways Scale

Anstine D. M., Zhao Q., Zubatiuk R., Zhang S., Singla V., Nikitin F., Savoie B. M., Isayev O.

(2025)

AIMNet2-rxn is a machine-learned interatomic potential trained on 4.7 10^6 range-separated DFT calculations that accelerates reaction modeling by about six orders of magnitude while retaining approximately 1–2 kcal/mol accuracy along reaction coordinates. By leveraging three‑dimensional chemical information and a batched nudged elastic band (BNEB) method, the model searches millions of reaction pathways and enables high‑throughput mechanistic analysis for complex transformations such as glucose pyrolysis.

Transferable Machine Learning Interatomic Potential for Pd-Catalyzed Cross-Coupling Reactions

Anstine D., Zubatyuk R., Gallegos L., Paton R., Wiest O., Nebgen B., Jones T., Gomes G., Tretiak S., Isayev O.

(2025)

Finding efficient substrate-catalyst combinations for palladium-catalyzed cross-coupling reactions remains a critical challenge in synthetic chemistry, with broad implications for pharmaceutical and materials manufacturing.

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

Anstine D. M., Zubatyuk R., Isayev O.

Chemical Science, 16, 10228–10244 (2025)

Machine learned interatomic potentials (MLIPs) are reshaping computational chemistry practices because of their ability to drastically exceed the accuracy-length/time scale tradeoff.

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

Avdiunina P., Jamal S., Gusev F., Isayev O.

Journal of Chemical Information and Modeling, 65, 10239–10252 (2025)

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

All that glitters is not gold: Importance of rigorous evaluation of proteochemometric models

Avdiunina P., Jamal S., Gusev F., Isayev O.

(2025)

Proteochemometric models (PCM) are used in computational drug discovery to leverage both protein and ligand representations for bioactivity prediction.

Democratizing Reaction Kinetics through Machine Vision and Learning

Baumer M., Gallegos L., Anstine D., Kubaney A., Regio J., Isayev O., Bernhard S., Gomes G.

(2025)

Democratizing Reaction Kinetics through Machine Vision and Learning.

Anticipating the Selectivity of Intramolecular Cyclization Reaction Pathways with Neural Network Potentials

Casetti N., Anstine D., Isayev O., Coley C. W.

Journal of Chemical Theory and Computation, 21, 10362–10372 (2025)

Anticipating the Selectivity of Intramolecular Cyclization Reaction Pathways with Neural Network Potentials.

AIQM3: Targeting Coupled-Cluster Accuracy with Semi-Empirical Speed Across Seven Main Group Elements

Chen Y., Hou Y., Zubatyuk R., Isayev O., Dral P. O.

(2025)

The AIQM series methods are successful neural network-based models that target coupled-cluster accuracy while maintaining high robustness and transferability across various tasks by leveraging Δ-learning.

Proto-Yield: An Uncertainty-Aware Prototype Network for Yield Prediction in Real-world Chemical Reactions

Guo K., Liu Z., Guo Z., Nan B., Isayev O., Chawla N., Wiest O., Zhang X.

, 791–801 (2025)

Proto-Yield: An Uncertainty-Aware Prototype Network for Yield Prediction in Real-world Chemical Reactions.

Machine Learning anomaly detection of automated HPLC experiments in the Cloud Laboratory

Gusev F., Kline B. C., Quinn R., Xu A., Smith B., Frezza B., Isayev O.

(2025)

Automation of experiments in cloud laboratories promises to revolutionize scientific research by enabling remote experimentation and improving reproducibility.

Machine learning anomaly detection of automated HPLC experiments in the cloud laboratory

Gusev F., Kline B. C., Quinn R., Xu A., Smith B., Frezza B., Isayev O.

Digital Discovery, 4, 3445–3454 (2025)

Autonomous experiments are vulnerable to unforeseen adverse events.

Machine learning interatomic potentials at the centennial crossroads of quantum mechanics

Kalita B., Gokcan H., Isayev O.

Nature Computational Science, 5, 1120–1132 (2025)

Machine learning interatomic potentials at the centennial crossroads of quantum mechanics.

AIMNet2‐NSE: A Transferable Reactive Neural Network Potential for Open‐Shell Chemistry

Kalita B., Zubatyuk R., Anstine D. M., Bergeler M., Settels V., Stork C., Spicher S., Isayev O.

Angewandte Chemie International Edition (2025)

Abstract Open‐shell systems such as radical intermediates are central to radical polymerization (RP), combustion, catalysis, and many other chemical and industrial processes, yet their accurate modeling presents significant computational challenges.

AIMNet2‐NSE: A Transferable Reactive Neural Network Potential for Open‐Shell Chemistry

Kalita B., Zubatyuk R., Anstine D. M., Bergeler M., Settels V., Stork C., Spicher S., Isayev O.

Angewandte Chemie (2025)

Abstract Open‐shell systems such as radical intermediates are central to radical polymerization (RP), combustion, catalysis, and many other chemical and industrial processes, yet their accurate modeling presents significant computational challenges.

Fast and Accurate Ring Strain Energy Predictions with Machine Learning and Application in Strain-Promoted Reactions

Liu Z., Vinskus J., Fu Y., Liu P., Noonan K. J. T., Isayev O.

JACS Au, 5, 4750–4761 (2025)

Fast and Accurate Ring Strain Energy Predictions with Machine Learning and Application in Strain-Promoted Reactions.

Efficient Molecular Crystal Structure Prediction and Stability Assessment with AIMNet2 Neural Network Potentials

Nayal K. S., O’Connor D., Zubatyuk R., Anstine D. M., Yang Y., Tom R., Deng W., Tang K., Marom N., Isayev O.

Crystal Growth & Design, 25, 9092–9106 (2025)

Efficient Molecular Crystal Structure Prediction and Stability Assessment with AIMNet2 Neural Network Potentials.

Scalable Low-Energy Molecular Conformer Generation with Quantum Mechanical Accuracy

Nikitin F., Anstine D. M., Zubatyuk R., Paliwal S. G., Isayev O.

(2025)

Molecular geometry is crucial for biological activity and chemical reactivity; however, computational methods for generating 3D structures are limited by the vast scale of conformational space and the complexities of stereochemistry.

GEOM-drugs revisited: toward more chemically accurate benchmarks for 3D molecule generation

Nikitin F., Dunn I., Koes D. R., Isayev O.

Digital Discovery, 4, 3282–3291 (2025)

Revisiting GEOM drugs: corrected metrics and novel energy-based structural benchmark enable rigorous evaluation of 3D molecule generative models.

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

Rapp J. L., Anstine D. M., Gusev F., Nikitin F., Yun K. H., Borden M. A., Bhat V., Isayev O., Leibfarth F. A.

Angewandte Chemie, 137 (2025)

Abstract The development of high‐performance elastomers for additive manufacturing requires overcoming complex property trade‐offs that challenge conventional material discovery pipelines.

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

Rapp J. L., Anstine D. M., Gusev F., Nikitin F., Yun K. H., Borden M. A., Bhat V., Isayev O., Leibfarth F. A.

Angewandte Chemie International Edition, 64 (2025)

Abstract The development of high‐performance elastomers for additive manufacturing requires overcoming complex property trade‐offs that challenge conventional material discovery pipelines.

Machine Learning-Accelerated Screening of Hydroquinone Analogs for Proton-Coupled Electron Transfer

Sarma R., Wang Y., Hebert D., Tran E., Shao C., Fu S., Cho I., Isayev O., Garcia-Bosch I.

(2025)

Proton-coupled electron transfer (PCET) mediated by hydroquinone and related molecules is key to natural and artificial energy conversion.

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

Zhang S., Zubatyuk R., Yang Y., Roitberg A., Isayev O.

Journal of Chemical Theory and Computation, 21, 4365–4374 (2025)

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

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

Zhang S., Chigaev M., Isayev O., Messerly R. A., Lubbers N.

Journal of Chemical Information and Modeling, 65, 4367–4380 (2025)

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

Discovery of Novel Celecoxib Polymorphs Using AIMNet2 Machine Learning Interatomic Potential

Zheng P., Abramov Y., Sun C. C., Isayev O.

(2025)

Polymorphism plays a pivotal role in defining the solid-state properties of pharmaceutical compounds, yet the discovery and accurate energy ranking of polymorphs remain a challenge.

High-throughput electronic property prediction of cyclic molecules with 3D-enhanced machine learning

Zheng P., Isayev O.

Chemical Science, 16, 20553–20563 (2025)

Ring Vault contains 201 546 cyclic molecules across 11 elements.

AIMNet2: A Neural Network Potential to Meet your Neutral, Charged, Organic, and Elemental-Organic Needs

Anstine D., Zubatyuk R., Isayev O.

(2024)

Machine learned interatomic potentials (MLIPs) are reshaping computational chemistry practices because of their ability to drastically exceed the accuracy-length/time scale tradeoff.

AIMNet2: A Neural Network Potential to Meet your Neutral, Charged, Organic, and Elemental-Organic Needs

Anstine D., Zubatyuk R., Isayev O.

(2024)

Machine learned interatomic potentials (MLIPs) are reshaping computational chemistry practices because of their ability to drastically exceed the accuracy-length/time scale tradeoff.

Uncertainty-Aware Yield Prediction with Multimodal Molecular Features

Chen J., Guo K., Liu Z., Isayev O., Zhang X.

, 38, 8274–8282 (2024)

Predicting chemical reaction yields is pivotal for efficient chemical synthesis, an area that focuses on the creation of novel compounds for diverse uses.

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

Dral P. O., Ge F., Hou Y., Zheng P., Chen Y., Barbatti M., Isayev O., Wang C., Xue B., Pinheiro Jr M., Su Y., Dai Y., Chen Y., Zhang L., Zhang S., Ullah A., Zhang Q., Ou Y.

J. Chem. Theory Comput., 20, 1193–1213 (2024)

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

Gutkin E., Gusev F., Gentile F., Ban F., Koby S. B., Narangoda C., Isayev O., Cherkasov A., Kurnikova M. G.

(2024)

The Critical Assessment of Computational Hit-Finding Experiments (CACHE) Challenge series is focused on identifying small molecule inhibitors of protein targets using computational methods.

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

Gutkin E., Gusev F., Gentile F., Ban F., Koby S. B., Narangoda C., Isayev O., Cherkasov A., Kurnikova M. G.

Chemical Science, 15, 8800–8812 (2024)

In this work, we combined Deep Docking and free energy MD simulations for the in silico screening and experimental validation for potential inhibitors of leucine rich repeat kinase 2 (LRRK2) targeting the WD40 repeat (WDR) domain.

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

Haghiri S., Viquez Rojas C., Bhat S., Isayev O., Slipchenko L.

Journal of Chemical Theory and Computation, 20, 9138–9147 (2024)

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

Discovery of Crystallizable Organic Semiconductors with Machine Learning

Johnson H. M., Gusev F., Dull J. T., Seo Y., Priestley R. D., Isayev O., Rand B. P.

J. Am. Chem. Soc., 146, 21583–21590 (2024)

Discovery of Crystallizable Organic Semiconductors with Machine Learning.

Discovery of Crystallizable Organic Semiconductors with Machine Learning

Johnson H. M., Gusev F., Dull J. T., Seo Y., Priestley R. D., Isayev O., Rand B. P.

(2024)

Crystalline organic semiconductors are known to have improved charge carrier mobility and exciton diffusion length in comparison to their amorphous counterparts.

Discovery of Crystallizable Organic Semiconductors with Machine Learning

Johnson H. M., Gusev F., Dull J. T., Seo Y., Priestley R. D., Isayev O., Rand B. P.

(2024)

Crystalline organic semiconductors are known to have improved charge carrier mobility and exciton diffusion length in comparison to their amorphous counterparts.

Accurate Ring Strain Energy Predictions with Machine Learning and Application in Strain-Promoted Reactions

Liu Z., Vinskus J., Fu Y., Liu P., Noonan K., Isayev O.

(2024)

Ring strain energy (RSE) is crucial for understanding molecular reactivity.

De novo molecule design towards biased properties via a deep generative framework and iterative transfer learning

Sattari K., Li D., Kalita B., Xie Y., Lighvan F. B., Isayev O., Lin J.

Digital Discovery, 3, 410–421 (2024)

The RRCGAN, validated through DFT, demonstrates success in generating chemically valid molecules targeting energy gap values with 75% of the generated molecules have RE of <20% of the targeted values.

Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR

Tropsha A., Isayev O., Varnek A., Schneider G., Cherkasov A.

Nat. Rev. Drug Discov., 23, 141–155 (2024)

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

Zhang S., Makoś M. Z., Jadrich R. B., Kraka E., Barros K., Nebgen B. T., Tretiak S., Isayev O., Lubbers N., Messerly R. A., Smith J. S.

Nature Chemistry, 16, 727–734 (2024)

Abstract Atomistic simulation has a broad range of applications from drug design to materials discovery.

AIMNet2: A Neural Network Potential to Meet your Neutral, Charged, Organic, and Elemental-Organic Needs

Anstine D., Zubatyuk R., Isayev O.

(2023)

Machine learned interatomic potentials (MLIPs) are reshaping computational chemistry practices because of their ability to drastically exceed the accuracy-length/time scale tradeoff.

Generative Models as an Emerging Paradigm in the Chemical Sciences

Anstine D. M., Isayev O.

J. Am. Chem. Soc., 145, 8736–8750 (2023)

Generative Models as an Emerging Paradigm in the Chemical Sciences.

Machine Learning Interatomic Potentials and Long-Range Physics

Anstine D. M., Isayev O.

J. Phys. Chem. A, 127, 2417–2431 (2023)

Machine Learning Interatomic Potentials and Long-Range Physics.

Themed collection on Insightful Machine Learning for Physical Chemistry

Clark A. E., Dral P. O., Tamblyn I., Isayev O.

Physical Chemistry Chemical Physics, 25, 22563–22564 (2023)

This themed collection includes a collection of articles on Insightful Machine Learning for Physical Chemistry.

Synergy of semiempirical models and machine learning in computational chemistry

Fedik N., Nebgen B., Lubbers N., Barros K., Kulichenko M., Li Y. W., Zubatyuk R., Messerly R., Isayev O., Tretiak S.

J. Chem. Phys., 159, 110901 (2023)

Catalyzed by enormous success in the industrial sector, many research programs have been exploring data-driven, machine learning approaches.

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

Gusev F., Gutkin E., Kurnikova M. G., Isayev O.

J. Chem. Inf. Model., 63, 583–594 (2023)

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

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

Gutkin E., Gusev F., Gentile F., Ban F., Koby S. B., Narangoda C., Isayev O., Cherkasov A., Kurnikova M. G.

(2023)

The Critical Assessment of Computational Hit-Finding Experiments (CACHE) Challenge is focused on identifying small molecule inhibitors of protein targets using computational methods.

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

Jaffrelot Inizan T., Pl{\'e} T., Adjoua O., Ren P., Gokcan H., Isayev O., Lagard{\`e}re L., Piquemal J.

Chem. Sci., 14, 5438–5452 (2023)

Deep-HP is a scalable extension of the Tinker-HP multi-GPU molecular dynamics (MD) package enabling the use of Pytorch/TensorFlow Deep Neural Network (DNN) models.

The Challenge of Balancing Model Sensitivity and Robustness in Predicting Yields: A Benchmarking Study of Amide Coupling Reactions

Liu Z., Moroz Y. S., Isayev O.

(2023)

Accurate prediction of reaction yield is the holy grail for computer-assisted synthesis, but current models have failed to generalize to large literature datasets.

The challenge of balancing model sensitivity and robustness in predicting yields: a benchmarking study of amide coupling reactions

Liu Z., Moroz Y. S., Isayev O.

Chem. Sci., 14, 10835–10846 (2023)

A sensitive model captures the reactivity cliffs but overfit to yield outliers.

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

Messerly R., Zhang S., Makoś M., Jadrich R., Kraka E., Barros K., Nebgen B., Tretiak S., Isayev O., Lubbers N., Smith J.

(2023)

Abstract Reactive chemistry atomistic simulation has a broad range of applications from drug design to energy to materials discovery.

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

Moayedpour S., Bier I., Wen W., Dardzinski D., Isayev O., Marom N.

J. Phys. Chem. C, 127, 10398–10410 (2023)

Structure Prediction of Epitaxial Organic Interfaces with Ogre, Demonstrated for Tetracyanoquinodimethane (TCNQ., materials science

Comprehensive exploration of graphically defined reaction spaces

Zhao Q., Vaddadi S. M., Woulfe M., Ogunfowora L. A., Garimella S. S., Isayev O., Savoie B. M.

Sci. Data, 10, 145 (2023)

Abstract Existing reaction transition state (TS) databases are comparatively small and lack chemical diversity.

$Δ^2$ machine learning for reaction property prediction

Zhao Q., Anstine D. M., Isayev O., Savoie B. M.

Chem. Sci., 14, 13392–13401 (2023)

Newly developed Δ 2 -learning models enable state-of-the-art accuracy in predicting the properties of chemical reactions.

Extending machine learning beyond interatomic potentials for predicting molecular properties

Fedik N., Zubatyuk R., Kulichenko M., Lubbers N., Smith J. S., Nebgen B., Messerly R., Li Y. W., Boldyrev A. I., Barros K., Isayev O., Tretiak S.

Nat. Rev. Chem., 6, 653–672 (2022)

Extending machine learning beyond interatomic potentials for predicting molecular properties.

Learning molecular potentials with neural networks

Gokcan H., Isayev O.

WIREs Comput. Mol. Sci., 12, e1564 (2022)

AbstractThe potential energy of molecular species and their conformers can be computed with a wide range of computational chemistry methods, from molecular mechanics to ab initio quantum chemistry.

Simulations of Pathogenic E1α Variants: Allostery and Impact on Pyruvate Dehydrogenase Complex-E1 Structure and Function

Gokcan H., Bedoyan J. K., Isayev O.

Journal of Chemical Information and Modeling, 62, 3463–3475 (2022)

Simulations of Pathogenic E1α Variants: Allostery and Impact on Pyruvate Dehydrogenase Complex-E1 Structure and Function.

Prediction of Protein pKa with Representation Learning

Gokcan H., Isayev O.

(2022)

The behavior of proteins is closely related to the protonation states of the residues.

Prediction of Protein pKa with Representation Learning

Gokcan H., Isayev O.

(2022)

The behavior of proteins is closely related to the protonation states of the residues.

Prediction of protein pKawith representation learning

Gokcan H., Isayev O.

Chemical Science, 13, 2462–2474 (2022)

We developed new empirical ML model for protein pKaprediction with MAEs below 0.

Active learning guided drug design lead optimization based on relative binding free energy modeling

Gusev F., Gutkin E., Kurnikova M. G., Isayev O.

(2022)

In silico identification of potent protein inhibitors commonly requires prediction of a ligand binding free energy (BFE).

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

Korshunova M., Huang N., Capuzzi S., Radchenko D. S., Savych O., Moroz Y. S., Wells C. I., Willson T. M., Tropsha A., Isayev O.

Communications Chemistry, 5 (2022)

AbstractDeep generative neural networks have been used increasingly in computational chemistry for de novo design of molecules with desired properties.

Roadmap on Machine learning in electronic structure

Kulik H. J., Hammerschmidt T., Schmidt J., Botti S., Marques M. A. L., Boley M., Scheffler M., Todorovi{\'c} M., Rinke P., Oses C., Smolyanyuk A., Curtarolo S., Tkatchenko A., Bart{\'o}k A. P., Manzhos S., Ihara M., Carrington T., Behler J., Isayev O., Veit M., Grisafi A., Nigam J., Ceriotti M., Sch{\"u}tt K. T., Westermayr J., Gastegger M., Maurer R. J., Kalita B., Burke K., Nagai R., Akashi R., Sugino O., Hermann J., No{\'e} F., Pilati S., Draxl C., Kuban M., Rigamonti S., Scheidgen M., Esters M., Hicks D., Toher C., Balachandran P. V., Tamblyn I., Whitelam S., Bellinger C., Ghiringhelli L. M.

Electron. Struct., 4, 023004 (2022)

AbstractIn recent years, we have been witnessing a paradigm shift in computational materials science.

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

Liu Z., Zubatiuk T., Roitberg A., Isayev O.

(2022)

Computational programs accelerate the chemical discovery processes but often need proper 3-dimensional molecular information as part of the input.

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

Liu Z., Zubatiuk T., Roitberg A., Isayev O.

(2022)

Computational programs accelerate the chemical discovery processes but often need proper 3-dimensional molecular information as part of the input.

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

Liu Z., Zubatiuk T., Roitberg A., Isayev O.

J. Chem. Inf. Model., 62, 5373–5382 (2022)

The transformational role of GPU computing and deep learning in drug discovery

Pandey M., Fernandez M., Gentile F., Isayev O., Tropsha A., Stern A. C., Cherkasov A.

Nature Machine Intelligence, 4, 211–221 (2022)

The transformational role of GPU computing and deep learning in drug discovery.

Toward Chemical Accuracy in Predicting Enthalpies of Formation with General-Purpose Data-Driven Methods

Zheng P., Yang W., Wu W., Isayev O., Dral P. O.

J. Phys. Chem. Lett., 13, 3479–3491 (2022)

Toward Chemical Accuracy in Predicting Enthalpies of Formation with General-Purpose Data-Driven Methods.

Best practices in machine learning for chemistry

Artrith N., Butler K. T., Coudert F., Han S., Isayev O., Jain A., Walsh A.

Nature Chemistry, 13, 505–508 (2021)

Best practices in machine learning for chemistry.

Crowdsourced mapping of unexplored target space of kinase inhibitors

Cichońska A., Ravikumar B., Allaway R. J., Wan F., Park S., Isayev O., Li S., Mason M., Lamb A., Tanoli Z., Jeon M., Kim S., Popova M., Capuzzi S., Zeng J., Dang K., Koytiger G., Kang J., Wells C. I., Willson T. M., Tan M., Huang C., Shih E. S. C., Chen T., Wu C., Fang W., Chen J., Hwang M., Wang X., Ben Guebila M., Shamsaei B., Singh S., Nguyen T., Karimi M., Wu D., Wang Z., Shen Y., Öztürk H., Ozkirimli E., Özgür A., Lim H., Xie L., Kanev G. K., Kooistra A. J., Westerman B. A., Terzopoulos P., Ntagiantas K., Fotis C., Alexopoulos L., Boeckaerts D., Stock M., De Baets B., Briers Y., Luo Y., Hu H., Peng J., Dogan T., Rifaioglu A. S., Atas H., Atalay R. C., Atalay V., Martin M. J., Jeon M., Lee J., Yun S., Kim B., Chang B., Turu G., Misák Á., Szalai B., Hunyady L., Lienhard M., Prasse P., Bachmann I., Ganzlin J., Barel G., Herwig R., Oršolić D., Lučić B., Stepanić V., Šmuc T., Oprea T. I., Schlessinger A., Drewry D. H., Stolovitzky G., Wennerberg K., Guinney J., Aittokallio T.

Nature Communications, 12 (2021)

Abstract Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged.

Harnessing the Power of Smart and Connected Health to Tackle COVID-19: IoT, AI, Robotics, and Blockchain for a Better World

Firouzi F., Farahani B., Daneshmand M., Grise K., Song J., Saracco R., Wang L. L., Lo K., Angelov P., Soares E., Loh P., Talebpour Z., Moradi R., Goodarzi M., Ashraf H., Talebpour M., Talebpour A., Romeo L., Das R., Heidari H., Pasquale D., Moody J., Woods C., Huang E. S., Barnaghi P., Sarrafzadeh M., Li R., Beck K. L., Isayev O., Sung N., Luo A.

IEEE Internet of Things Journal, 8, 12826–12846 (2021)

Harnessing the Power of Smart and Connected Health to Tackle COVID-19: IoT, AI, Robotics, and Blockchain for a Better World.

Active Learning in Bayesian Neural Networks for Bandgap Predictions of Novel Van der Waals Heterostructures

Fronzi M., Isayev O., Winkler D. A., Shapter J. G., Ellis A. V., Sherrell P. C., Shepelin N. A., Corletto A., Ford M. J.

Advanced Intelligent Systems, 3 (2021)

The bandgap is one of the most fundamental properties of condensed matter.

Prediction of Protein pKa with Representation Learning

Gokcan H., Isayev O.

(2021)

The behavior of proteins is closely related to the protonation states of the residues.

Prediction of Protein pKa with Representation Learning

Gokcan H., Isayev O.

(2021)

The behavior of proteins is closely related to the protonation states of the residues.

A Bag of Tricks for Automated De Novo Design of Molecules with the Desired Properties: Application to EGFR Inhibitor Discovery

Korshunova M., Huang N., Capuzzi S., Radchenko D. S., Savych O., Moroz Y. S., Wells C., Willson T. M., Tropsha A., Isayev O.

(2021)

Deep generative neural networks have been used increasingly in computational chemistry for de novo design of molecules with desired properties.

OpenChem: A Deep Learning Toolkit for Computational Chemistry and Drug Design

Korshunova M., Ginsburg B., Tropsha A., Isayev O.

Journal of Chemical Information and Modeling, 61, 7–13 (2021)

OpenChem: A Deep Learning Toolkit for Computational Chemistry and Drug Design.

A critical overview of computational approaches employed for COVID-19 drug discovery

Muratov E. N., Amaro R., Andrade C. H., Brown N., Ekins S., Fourches D., Isayev O., Kozakov D., Medina-Franco J. L., Merz K. M., Oprea T. I., Poroikov V., Schneider G., Todd M. H., Varnek A., Winkler D. A., Zakharov A. V., Cherkasov A., Tropsha A.

Chemical Society Reviews, 50, 9121–9151 (2021)

We cover diverse methodologies, computational approaches, and case studies illustrating the ongoing efforts to develop viable drug candidates for treatment of COVID-19.

Machine-Learning-Guided Discovery of 19 F MRI Agents Enabled by Automated Copolymer Synthesis

Reis M., Gusev F., Taylor N. G., Chung S. H., Verber M. D., Lee Y. Z., Isayev O., Leibfarth F. A.

Journal of the American Chemical Society, 143, 17677–17689 (2021)

Machine-Learning-Guided Discovery of 19 F MRI Agents Enabled by Automated Copolymer Synthesis.

Artificial intelligence-enhanced quantum chemical method with broad applicability

Zheng P., Zubatyuk R., Wu W., Isayev O., Dral P. O.

Nature Communications, 12 (2021)

Abstract High-level quantum mechanical (QM) calculations are indispensable for accurate explanation of natural phenomena on the atomistic level.

Machine learned Hückel theory: Interfacing physics and deep neural networks

Zubatiuk T., Nebgen B., Lubbers N., Smith J. S., Zubatyuk R., Zhou G., Koh C., Barros K., Isayev O., Tretiak S.

The Journal of Chemical Physics, 154 (2021)

The Hückel Hamiltonian is an incredibly simple tight-binding model known for its ability to capture qualitative physics phenomena arising from electron interactions in molecules and materials.

Development of Multimodal Machine Learning Potentials: Toward a Physics-Aware Artificial Intelligence

Zubatiuk T., Isayev O.

Accounts of Chemical Research, 54, 1575–1585 (2021)

Development of Multimodal Machine Learning Potentials: Toward a Physics-Aware Artificial Intelligence.

Teaching a neural network to attach and detach electrons from molecules

Zubatyuk R., Smith J. S., Nebgen B. T., Tretiak S., Isayev O.

Nature Communications, 12 (2021)

Abstract Interatomic potentials derived with Machine Learning algorithms such as Deep-Neural Networks (DNNs), achieve the accuracy of high-fidelity quantum mechanical (QM) methods in areas traditionally dominated by empirical force fields and allow performing massive simulations.

Teaching a Neural Network to Attach and Detach Electrons from Molecules

Zubatyuk R., Smith J., Nebgen B. T., Tretiak S., Isayev O.

(2021)

Physics-inspired Artificial Intelligence (AI) is at the forefront of methods development in molecular modeling and computational chemistry.

Crowdsourced mapping extends the target space of kinase inhibitors

Cichonska A., Ravikumar B., Allaway R. J., Park S., Wan F., Isayev O., Li S., Mason M., Lamb A., Tanoli Z., Jeon M., Kim S., Popova M., Capuzzi S., Zeng J., Dang K., Koytiger G., Kang J., Wells C. I., Willson T. M., Oprea T. I., Schlessinger A., Drewry D. H., Stolovitzky G., Wennerberg K., Guinney J., Aittokallio T.

(2020)

AbstractDespite decades of intensive search for compounds that modulate the activity of particular targets, there are currently small-molecules available only for a small proportion of the human proteome.

Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens

Devereux C., Smith J. S., Huddleston K. K., Barros K., Zubatyuk R., Isayev O., Roitberg A. E.

Journal of Chemical Theory and Computation, 16, 4192–4202 (2020)

Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens.

Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens

Devereux C., Smith J., Davis K., Barros K., Zubatyuk R., Isayev O., Roitberg A.

(2020)

Machine learning (ML) methods have become powerful, predictive tools in a wide range of applications, such as facial recognition and autonomous vehicles.

High Throughput Screening of Millions of van der Waals Heterostructures for Superlubricant Applications

Fronzi M., Tawfik S. A., Ghazaleh M. A., Isayev O., Winkler D. A., Shapter J., Ford M. J.

Advanced Theory and Simulations, 3 (2020)

AbstractThe screening of novel materials is an important topic in the field of materials science.

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

Gao X., Ramezanghorbani F., Isayev O., Smith J. S., Roitberg A. E.

Journal of Chemical Information and Modeling, 60, 3408–3415 (2020)

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

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

Gao X., Ramezanghorbani F., Isayev O., Smith J., Roitberg A.

(2020)

This paper presents TorchANI, a PyTorch based software for training/inferenceof ANI (ANAKIN-ME) deep learning models to obtain potential energy surfaces andother physical properties of molecular systems.

Review for: Assessing Conformer Energies using Electronic Structure and Machine Learning Methods

Isayev O.

(2020)

Review for: Assessing Conformer Energies using Electronic Structure and Machine Learning Methods.

Correction: QSAR without borders

Muratov E. N., Bajorath J., Sheridan R. P., Tetko I. V., Filimonov D., Poroikov V., Oprea T. I., Baskin I. I., Varnek A., Roitberg A., Isayev O., Curtarolo S., Fourches D., Cohen Y., Aspuru-Guzik A., Winkler D. A., Agrafiotis D., Cherkasov A., Tropsha A.

Chemical Society Reviews, 49, 3716–3716 (2020)

Correction: QSAR without borders.

QSAR without borders

Muratov E. N., Bajorath J., Sheridan R. P., Tetko I. V., Filimonov D., Poroikov V., Oprea T. I., Baskin I. I., Varnek A., Roitberg A., Isayev O., Curtalolo S., Fourches D., Cohen Y., Aspuru-Guzik A., Winkler D. A., Agrafiotis D., Cherkasov A., Tropsha A.

Chemical Society Reviews, 49, 3525–3564 (2020)

Word cloud summary of diverse topics associated with QSAR modeling that are discussed in this review.

DRACON: Disconnected Graph Neural Network for Atom Mapping in Chemical Reactions

Nikitin F., Isayev O., Strijov V.

(2020)

Machine learning solved many challenging problems in computer-assisted synthesis prediction (CASP).

DRACON: Disconnected Graph Neural Network for Atom Mapping in Chemical Reactions

Nikitin F., Isayev O., Strijov V.

(2020)

Machine learning solved many challenging problems in computer-assisted synthesis prediction (CASP).

DRACON: Disconnected Graph Neural Network for Atom Mapping in Chemical Reactions

Nikitin F., Isayev O., Strijov V.

(2020)

Machine learning solved many challenging problems in computer-assisted synthesis prediction (CASP).

DRACON: disconnected graph neural network for atom mapping in chemical reactions

Nikitin F., Isayev O., Strijov V.

Physical Chemistry Chemical Physics, 22, 26478–26486 (2020)

We formulate a reaction prediction problem in terms of node-classification in a disconnected graph of source molecules and generalize a graph convolution neural network for disconnected graphs.

OpenChem: A Deep Learning Toolkit for Computational Chemistry and Drug Design

Popova M., Ginsburg B., Tropsha A., Isayev O.

(2020)

Deep learning models have demonstrated outstanding results in many data-rich areas of research, such as computer vision and natural language processing.

Towards chemical accuracy for alchemical free energy calculations with hybrid physics-based machine learning / molecular mechanics potentials

Rufa D. A., Bruce Macdonald H. E., Fass J., Wieder M., Grinaway P. B., Roitberg A. E., Isayev O., Chodera J. D.

(2020)

Towards chemical accuracy for alchemical free energy calculations with hybrid physics-based machine learning / molecular mechanics potentials.

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

Smith J. S., Zubatyuk R., Nebgen B., Lubbers N., Barros K., Roitberg A. E., Isayev O., Tretiak S.

Scientific Data, 7 (2020)

Abstract Maximum diversification of data is a central theme in building generalized and accurate machine learning (ML) models.

The ANI-1ccx and ANI-1x Data Sets, Coupled-Cluster and Density Functional Theory Properties for Molecules

Smith J. S., Zubatyuk R., Nebgen B. T., Lubbers N., Barros K., Roitberg A., Isayev O., Tretiak S.

(2020)

Maximum diversification of data is a central theme in building generalized and accurate machine learning (ML) models.

The ANI-1ccx and ANI-1x Data Sets, Coupled-Cluster and Density Functional Theory Properties for Molecules

Smith J. S., Zubatyuk R., Nebgen B. T., Lubbers N., Barros K., Roitberg A., Isayev O., Tretiak S.

(2020)

Maximum diversification of data is a central theme in building generalized and accurate machine learning (ML) models.

Teaching a Neural Network to Attach and Detach Electrons from Molecules

Zubatyuk R., Smith J., Nebgen B. T., Tretiak S., Isayev O.

(2020)

Physics-inspired Artificial Intelligence (AI) is at the forefront of methods development in molecular modeling and computational chemistry.

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

Farag S., Bleich R. M., Shank E. A., Isayev O., Bowers A. A., Tropsha A.

Bioinformatics, 35, 3584–3591 (2019)

Abstract Motivation Non-ribosomal peptide synthetases (NRPSs) are modular enzymatic machines that catalyze the ribosome-independent production of structurally complex small peptides, many of which have important clinical applications as antibiotics, antifungals and anti-cancer agents.

Quantitative Structure–Price Relationship (QS$R) Modeling and the Development of Economically Feasible Drug Discovery Projects

Fernandez M., Ban F., Woo G., Isaev O., Perez C., Fokin V., Tropsha A., Cherkasov A.

Journal of Chemical Information and Modeling, 59, 1306–1313 (2019)

Quantitative Structure–Price Relationship (QS$R) Modeling and the Development of Economically Feasible Drug Discovery Projects.

Text mining facilitates materials discovery

Isayev O.

Nature, 571, 42–43 (2019)

Text mining facilitates materials discovery.

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

Menden M. P., Wang D., Mason M. J., Szalai B., Bulusu K. C., Guan Y., Yu T., Kang J., Jeon M., Wolfinger R., Nguyen T., Zaslavskiy M., Abante J., Abecassis B. S., Aben N., Aghamirzaie D., Aittokallio T., Akhtari F. S., Al-lazikani B., Alam T., Allam A., Allen C., de Almeida M. P., Altarawy D., Alves V., Amadoz A., Anchang B., Antolin A. A., Ash J. R., Aznar V. R., Ba-alawi W., Bagheri M., Bajic V., Ball G., Ballester P. J., Baptista D., Bare C., Bateson M., Bender A., Bertrand D., Wijayawardena B., Boroevich K. A., Bosdriesz E., Bougouffa S., Bounova G., Brouwer T., Bryant B., Calaza M., Calderone A., Calza S., Capuzzi S., Carbonell-Caballero J., Carlin D., Carter H., Castagnoli L., Celebi R., Cesareni G., Chang H., Chen G., Chen H., Chen H., Cheng L., Chernomoretz A., Chicco D., Cho K., Cho S., Choi D., Choi J., Choi K., Choi M., Cock M. D., Coker E., Cortes-Ciriano I., Cserzö M., Cubuk C., Curtis C., Daele D. V., Dang C. C., Dijkstra T., Dopazo J., Draghici S., Drosou A., Dumontier M., Ehrhart F., Eid F., ElHefnawi M., Elmarakeby H., van Engelen B., Engin H. B., de Esch I., Evelo C., Falcao A. O., Farag S., Fernandez-Lozano C., Fisch K., Flobak A., Fornari C., Foroushani A. B. K., Fotso D. C., Fourches D., Friend S., Frigessi A., Gao F., Gao X., Gerold J. M., Gestraud P., Ghosh S., Gillberg J., Godoy-Lorite A., Godynyuk L., Godzik A., Goldenberg A., Gomez-Cabrero D., Gonen M., de Graaf C., Gray H., Grechkin M., Guimera R., Guney E., Haibe-Kains B., Han Y., Hase T., He D., He L., Heath L. S., Hellton K. H., Helmer-Citterich M., Hidalgo M. R., Hidru D., Hill S. M., Hochreiter S., Hong S., Hovig E., Hsueh Y., Hu Z., Huang J. K., Huang R. S., Hunyady L., Hwang J., Hwang T. H., Hwang W., Hwang Y., Isayev O., Don’t Walk O. B., Jack J., Jahandideh S., Ji J., Jo Y., Kamola P. J., Kanev G. K., Karacosta L., Karimi M., Kaski S., Kazanov M., Khamis A. M., Khan S. A., Kiani N. A., Kim A., Kim J., Kim J., Kim K., Kim K., Kim S., Kim Y., Kim Y., Kirk P. D. W., Kitano H., Klambauer G., Knowles D., Ko M., Kohn-Luque A., Kooistra A. J., Kuenemann M. A., Kuiper M., Kurz C., Kwon M., van Laarhoven T., Laegreid A., Lederer S., Lee H., Lee J., Lee Y. W., Lepp_aho E., Lewis R., Li J., Li L., Liley J., Lim W. K., Lin C., Liu Y., Lopez Y., Low J., Lysenko A., Machado D., Madhukar N., Maeyer D. D., Malpartida A. B., Mamitsuka H., Marabita F., Marchal K., Marttinen P., Mason D., Mazaheri A., Mehmood A., Mehreen A., Michaut M., Miller R. A., Mitsopoulos C., Modos D., Moerbeke M. V., Moo K., Motsinger-Reif A., Movva R., Muraru S., Muratov E., Mushthofa M., Nagarajan N., Nakken S., Nath A., Neuvial P., Newton R., Ning Z., Niz C. D., Oliva B., Olsen C., Palmeri A., Panesar B., Papadopoulos S., Park J., Park S., Park S., Pawitan Y., Peluso D., Pendyala S., Peng J., Perfetto L., Pirro S., Plevritis S., Politi R., Poon H., Porta E., Prellner I., Preuer K., Pujana M. A., Ramnarine R., Reid J. E., Reyal F., Richardson S., Ricketts C., Rieswijk L., Rocha M., Rodriguez-Gonzalvez C., Roell K., Rotroff D., de Ruiter J. R., Rukawa P., Sadacca B., Safikhani Z., Safitri F., Sales-Pardo M., Sauer S., Schlichting M., Seoane J. A., Serra J., Shang M., Sharma A., Sharma H., Shen Y., Shiga M., Shin M., Shkedy Z., Shopsowitz K., Sinai S., Skola D., Smirnov P., Soerensen I. F., Soerensen P., Song J., Song S. O., Soufan O., Spitzmueller A., Steipe B., Suphavilai C., Tamayo S. P., Tamborero D., Tang J., Tanoli Z., Tarres-Deulofeu M., Tegner J., Thommesen L., Tonekaboni S. A. M., Tran H., Troyer E. D., Truong A., Tsunoda T., Turu G., Tzeng G., Verbeke L., Videla S., Vis D., Voronkov A., Votis K., Wang A., Wang H. H., Wang P., Wang S., Wang W., Wang X., Wang X., Wennerberg K., Wernisch L., Wessels L., van Westen G. J. P., Westerman B. A., White S. R., Willighagen E., Wurdinger T., Xie L., Xie S., Xu H., Yadav B., Yau C., Yeerna H., Yin J. W., Yu M., Yu M., Yun S. J., Zakharov A., Zamichos A., Zanin M., Zeng L., Zenil H., Zhang F., Zhang P., Zhang W., Zhao H., Zhao L., Zheng W., Zoufir A., Zucknick M., Jang I. S., Ghazoui Z., Ahsen M. E., Vogel R., Neto E. C., Norman T., Tang E. K. Y., Garnett M. J., Veroli G. Y. D., Fawell S., Stolovitzky G., Guinney J., Dry J. R., Saez-Rodriguez J.

Nature Communications, 10 (2019)

Abstract The effectiveness of most cancer targeted therapies is short-lived.

The ANI-1ccx and ANI-1x Data Sets, Coupled-Cluster and Density Functional Theory Properties for Molecules

Smith J. S., Zubatyuk R., Nebgen B. T., Lubbers N., Barros K., Roitberg A., Isayev O., Tretiak S.

(2019)

Maximum diversification of data is a central theme in building generalized and accurate machine learning (ML) models.

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

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

Nature Communications, 10 (2019)

Abstract Computational modeling of chemical and biological systems at atomic resolution is a crucial tool in the chemist’s toolset.

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

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

(2019)

Computational modeling of chemical and biological systems at atomic resolution is a crucial tool in the chemist's toolset.

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

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

(2019)

Computational modeling of chemical and biological systems at atomic resolution is a crucial tool in the chemist's toolset.

Predicting Thermal Properties of Crystals Using Machine Learning

Tawfik S. A., Isayev O., Spencer M. J. S., Winkler D. A.

Advanced Theory and Simulations, 3 (2019)

AbstractCalculating vibrational properties of crystals using quantum mechanical (QM) methods is a challenging problem in computational material science.

Adsorption of nitrogen-containing compounds on hydroxylated α-quartz surfaces

Tsendra O., Boese A. D., Isayev O., Gorb L., Scott A. M., Hill F. C., Ilchenko M. M., Lobanov V., Leszczynska D., Leszczynski J.

RSC Advances, 9, 36066–36074 (2019)

Adsorption energies of different nitrogen-containing compounds on two hydroxylated (001) and (100) quartz surfaces are computed.

Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network

Zubatyuk R., Smith J. S., Leszczynski J., Isayev O.

Science Advances, 5 (2019)

We introduce a modular, chemically inspired deep neural network model for prediction of several atomic and molecular properties.

Machine learning for molecular and materials science

Butler K. T., Davies D. W., Cartwright H., Isayev O., Walsh A.

Nature, 559, 547–555 (2018)

Machine learning for molecular and materials science.

Materials discovery by chemical analogy: role of oxidation states in structure prediction

Davies D. W., Butler K. T., Isayev O., Walsh A.

Faraday Discussions, 211, 553–568 (2018)

We have built a model that ascribes probabilities to the formation of hypothetical compounds, given the proposed oxidation states of the constituent species.

Diffusion of energetic compounds through biological membrane: Application of classical MD and COSMOmic approximations

Golius A., Gorb L., Isayev O., Leszczynski J.

Journal of Biomolecular Structure and Dynamics, 37, 247–255 (2018)

Diffusion of energetic compounds through biological membrane: Application of classical MD and COSMOmic approximations.

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

Gossett E., Toher C., Oses C., Isayev O., Legrain F., Rose F., Zurek E., Carrete J., Mingo N., Tropsha A., Curtarolo S.

Computational Materials Science, 152, 134–145 (2018)

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

Transferable Dynamic Molecular Charge Assignment Using Deep Neural Networks

Nebgen B., Lubbers N., Smith J. S., Sifain A. E., Lokhov A., Isayev O., Roitberg A. E., Barros K., Tretiak S.

Journal of Chemical Theory and Computation, 14, 4687–4698 (2018)

Transferable Dynamic Molecular Charge Assignment Using Deep Neural Networks.

Deep reinforcement learning for de novo drug design

Popova M., Isayev O., Tropsha A.

Science Advances, 4 (2018)

We introduce an artificial intelligence approach to de novo design of molecules with desired physical or biological properties.

Discovering a Transferable Charge Assignment Model Using Machine Learning

Sifain A. E., Lubbers N., Nebgen B. T., Smith J. S., Lokhov A. Y., Isayev O., Roitberg A. E., Barros K., Tretiak S.

(2018)

Partial atomic charge assignment is of immense practical value to force field parametrization, molecular docking, and cheminformatics.

Discovering a Transferable Charge Assignment Model Using Machine Learning

Sifain A. E., Lubbers N., Nebgen B. T., Smith J. S., Lokhov A. Y., Isayev O., Roitberg A. E., Barros K., Tretiak S.

The Journal of Physical Chemistry Letters, 9, 4495–4501 (2018)

Discovering a Transferable Charge Assignment Model Using Machine Learning.

Transforming Computational Drug Discovery with Machine Learning and AI

Smith J. S., Roitberg A. E., Isayev O.

ACS Medicinal Chemistry Letters, 9, 1065–1069 (2018)

Transforming Computational Drug Discovery with Machine Learning and AI.

Less is more: Sampling chemical space with active learning

Smith J. S., Nebgen B., Lubbers N., Isayev O., Roitberg A. E.

The Journal of Chemical Physics, 148 (2018)

The development of accurate and transferable machine learning (ML) potentials for predicting molecular energetics is a challenging task.

Efficient Prediction of Structural and Electronic Properties of Hybrid 2D Materials Using Complementary DFT and Machine Learning Approaches

Tawfik S. A., Isayev O., Stampfl C., Shapter J., Winkler D. A., Ford M. J.

Advanced Theory and Simulations, 2 (2018)

Abstract There are now, in principle, a limitless number of hybrid van der Waals (vdW) heterostructures that can be built from the rapidly growing number of 2D layers.

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

Tawfik S., Isayev O., Stampfl C., Shapter J., Winkler D., Ford M. J.

(2018)

There are now, in principle, a limitless number of hybrid van der Waals heterostructures that can be built from the rapidly growing number of two-dimensional layers.

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

Tawfik S., Isayev O., Stampfl C., Shapter J., Winkler D., Ford M. J.

(2018)

There are now, in principle, a limitless number of hybrid van der Waals heterostructures that can be built from the rapidly growing number of two-dimensional layers.

Efficient Prediction of Structural and Electronic Properties of Hybrid 2D Materials Using DFT and Machine Learning

Tawfik S., Isayev O., Stampfl C., Shapter J., Winkler D., Ford M. J.

(2018)

Efficient Prediction of Structural and Electronic Properties of Hybrid 2D Materials Using DFT and Machine Learning.

Efficient Prediction of Structural and Electronic Properties of Hybrid 2D Materials Using DFT and Machine Learning

Tawfik S., Isayev O., Stampfl C., Shapter J., Winkler D., Ford M. J.

(2018)

Efficient Prediction of Structural and Electronic Properties of Hybrid 2D Materials Using DFT and Machine Learning.

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

Zubatyuk R., Smith J. S., Leszczynski J., Isayev O.

(2018)

Atomic and molecular properties could be evaluated from the fundamental Schrodinger’s equation and therefore represent different modalities of the same quantum phenomena.

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

Zubatyuk R., Smith J. S., Leszczynski J., Isayev O.

(2018)

Atomic and molecular properties could be evaluated from the fundamental Schrodinger’s equation and therefore represent different modalities of the same quantum phenomena.

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

Zubatyuk R., Smith J. S., Leszczynski J., Isayev O.

(2018)

Atomic and molecular properties could be evaluated from the fundamental Schrodinger’s equation and therefore represent different modalities of the same quantum phenomena.

Universal fragment descriptors for predicting properties of inorganic crystals

Isayev O., Oses C., Toher C., Gossett E., Curtarolo S., Tropsha A.

Nature Communications, 8 (2017)

AbstractAlthough historically materials discovery has been driven by a laborious trial-and-error process, knowledge-driven materials design can now be enabled by the rational combination of Machine Learning methods and materials databases.

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

Smith J. S., Isayev O., Roitberg A. E.

Scientific Data, 4 (2017)

AbstractOne of the grand challenges in modern theoretical chemistry is designing and implementing approximations that expedite ab initio methods without loss of accuracy.

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

Smith J. S., Isayev O., Roitberg A. E.

Chemical Science, 8, 3192–3203 (2017)

We demonstrate how a deep neural network (NN) trained on a data set of quantum mechanical (QM) DFT calculated energies can learn an accurate and transferable atomistic potential for organic molecules containing H, C, N, and O atoms.

QSAR Modeling of Tox21 Challenge Stress Response and Nuclear Receptor Signaling Toxicity Assays

Capuzzi S. J., Politi R., Isayev O., Farag S., Tropsha A.

Frontiers in Environmental Science, 4 (2016)

QSAR Modeling of Tox21 Challenge Stress Response and Nuclear Receptor Signaling Toxicity Assays.

Atlas Regeneration Company, Inc.

Makarev E., Isayev O., Atala A.

Regenerative Medicine, 11, 141–143 (2016)

Atlas Regeneration Company, Inc..

Material informatics driven design and experimental validation of lead titanate as an aqueous solar photocathode

Moot T., Isayev O., Call R. W., McCullough S. M., Zemaitis M., Lopez R., Cahoon J. F., Tropsha A.

Materials Discovery, 6, 9–16 (2016)

Material informatics driven design and experimental validation of lead titanate as an aqueous solar photocathode.

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

Isayev O., Fourches D., Muratov E. N., Oses C., Rasch K., Tropsha A., Curtarolo S.

Chemistry of Materials, 27, 735–743 (2015)

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 produced when adsorbed on a silica surface? A DFT M05-2X computational study

Sviatenko L. K., Isayev O., Gorb L., Hill F. C., Leszczynska D., Leszczynski J.

Journal of Computational Chemistry, 36, 1029–1035 (2015)

Are the reduction and oxidation properties of nitrocompounds dissolved in water different from those produced when adsorbed on a silica surface? A DFT M05-2X computational study.

Validation of a novel secretion modification region (SMR) of HIV-1 Nef using cohort sequence analysis and molecular modeling

Campbell P. E., Isayev O., Ali S. A., Roth W. W., Huang M., Powell M. D., Leszczynski J., Bond V. C.

Journal of Molecular Modeling, 18, 4603–4613 (2012)

Validation of a novel secretion modification region (SMR) of HIV-1 Nef using cohort sequence analysis and molecular modeling.

Mechanical properties of silicon nanowires

Furmanchuk A., Isayev O., Dinadayalane T. C., Leszczynska D., Leszczynski J.

WIREs Computational Molecular Science, 2, 817–828 (2012)

AbstractSilicon nanowires (SiNWs) are at the top of the list of materials used in conventional electromechanical devices as well as in strained nanotechnology.

In silico structure–function analysis of E. cloacae nitroreductase

Isayev O., Crespo‐Hernández C. E., Gorb L., Hill F. C., Leszczynski J.

Proteins: Structure, Function, and Bioinformatics, 80, 2728–2741 (2012)

AbstractReduction, catalyzed by the bacterial nitroreductases, is the quintessential first step in the biodegradation of a variety of nitroaromatic compounds from contaminated waters and soil.

Evaluation of natural and nitramine binding energies to 3-D models of the S1S2 domains in the N-methyl-D-aspartate receptor

Ford-Green J., Isayev O., Gorb L., Perkins E. J., Leszczynski J.

Journal of Molecular Modeling, 18, 1273–1284 (2011)

Evaluation of natural and nitramine binding energies to 3-D models of the S1S2 domains in the N-methyl-D-aspartate receptor.

Car–Parrinello Molecular Dynamics Simulations of Tensile Tests on Si⟨001⟩ Nanowires

Furmanchuk A., Isayev O., Dinadayalane T. C., Leszczynski J.

The Journal of Physical Chemistry C, 115, 12283–12292 (2011)

Car–Parrinello Molecular Dynamics Simulations of Tensile Tests on Si⟨001⟩ Nanowires.

Novel view on the mechanism of water-assisted proton transfer in the DNA bases: bulk water hydration

Furmanchuk A., Isayev O., Gorb L., Shishkin O. V., Hovorun D. M., Leszczynski J.

Physical Chemistry Chemical Physics, 13, 4311 (2011)

Novel view on the mechanism of water-assisted proton transfer in the DNA bases: bulk water hydration.

Effect of Solvation on the Vertical Ionization Energy of Thymine: From Microhydration to Bulk

Ghosh D., Isayev O., Slipchenko L. V., Krylov A. I.

The Journal of Physical Chemistry A, 115, 6028–6038 (2011)

Effect of Solvation on the Vertical Ionization Energy of Thymine: From Microhydration to Bulk.

Toward robust computational electrochemical predicting the environmental fate of organic pollutants

Sviatenko L., Isayev O., Gorb L., Hill F., Leszczynski J.

Journal of Computational Chemistry, 32, 2195–2203 (2011)

AbstractA number of density functionals was utilized for the calculation of electron attachment free energy for nitrocompounds, quinones and azacyclic compounds.

Hydration of nucleic acid bases: a Car–Parrinello molecular dynamics approach

Furmanchuk A., Isayev O., Shishkin O. V., Gorb L., Leszczynski J.

Physical Chemistry Chemical Physics, 12, 3363 (2010)

Hydration of nucleic acid bases: a Car–Parrinello molecular dynamics approach.

New insight on structural properties of hydrated nucleic acid bases from ab initio molecular dynamics

Furmanchuk A., Shishkin O. V., Isayev O., Gorb L., Leszczynski J.

Physical Chemistry Chemical Physics, 12, 9945 (2010)

New insight on structural properties of hydrated nucleic acid bases from ab initio molecular dynamics.

Reaction of bicyclo[2.2.1]hept‐5‐ene‐endo‐2‐ylmethylamine and nitrophenyl glycidyl ethers

Kasyan L. I., Prid’ma S. A., Palchikov V. A., Karat L. D., Turov A. V., Isayev O.

Journal of Physical Organic Chemistry, 24, 705–713 (2010)

AbstractReactions of 2‐nitro‐, 4‐nitro‐ and 2,4‐dinitrophenylglycidyl ethers with bicyclo[2.

One-electron standard reduction potentials of nitroaromatic and cyclic nitramine explosives

Uchimiya M., Gorb L., Isayev O., Qasim M. M., Leszczynski J.

Environmental Pollution, 158, 3048–3053 (2010)

One-electron standard reduction potentials of nitroaromatic and cyclic nitramine explosives.

Ab Initio Molecular Dynamics Study on the Initial Chemical Events in Nitramines: Thermal Decomposition of CL-20

Isayev O., Gorb L., Qasim M., Leszczynski J.

The Journal of Physical Chemistry B, 112, 11005–11013 (2008)

Ab Initio Molecular Dynamics Study on the Initial Chemical Events in Nitramines: Thermal Decomposition of CL-20.

Efficient and accurate ab initio prediction of thermodynamic parameters for intermolecular complexes

Isayev O., Furmanchuk A., Gorb L., Leszczynski J.

Chemical Physics Letters, 451, 147–152 (2008)

Efficient and accurate ab initio prediction of thermodynamic parameters for intermolecular complexes.

Are Isolated Nucleic Acid Bases Really Planar? A Car−Parrinello Molecular Dynamics Study

Isayev O., Furmanchuk A., Shishkin O. V., Gorb L., Leszczynski J.

The Journal of Physical Chemistry B, 111, 3476–3480 (2007)

Are Isolated Nucleic Acid Bases Really Planar? A Car−Parrinello Molecular Dynamics Study.

Electronic Structure and Bonding of Fe(PhNO2)6 Complexes: A Density Functional Theory Study

Isayev O., Gorb L., Zilberberg I., Leszczynski J.

The Journal of Physical Chemistry A, 111 (2007)

Electronic Structure and Bonding of Fe(PhNO2)., pages=3571–3576

Theoretical calculations: Can Gibbs free energy for intermolecular complexes be predicted efficiently and accurately?

Isayev O., Gorb L., Leszczynski J.

Journal of Computational Chemistry, 28, 1598–1609 (2007)

AbstractThe theoretical study has been performed to refine the procedure for calculations of Gibbs free energy with a relative accuracy of less than 1 kcal/mol.

Carboxamides and amines having two and three adamantane fragments

Kas’yan L. I., Karpenko D. V., Kas’yan A. O., Isaev A. K., Prid’ma S. A.

Russian Journal of Organic Chemistry, 43, 1642–1650 (2007)

Carboxamides and amines having two and three adamantane fragments.

Structure-toxicity relationships of nitroaromatic compounds: Full-length paper

Isayev O., Rasulev B., Gorb L., Leszczynski J.

Molecular Diversity, 10, 233–245 (2006)

Structure-toxicity relationships of nitroaromatic compounds: Full-length paper.

Acylation of Aminopyridines and Related Compounds with Endic Anhydride

Kas’yan L. I., Tarabara I. N., Pal’chikov V. A., Krishchik O. V., Isaev A. K., Kas’yan A. O.

Russian Journal of Organic Chemistry, 41, 1530–1538 (2005)

Acylation of Aminopyridines and Related Compounds with Endic Anhydride.

Synthesis and Reactivity of Amines Containing Several Cage-like Fragments

Kas’yan L. I., Karpenko D. V., Kas’yan A. O., Isaev A. K.

Russian Journal of Organic Chemistry, 41, 678–688 (2005)

Synthesis and Reactivity of Amines Containing Several Cage-like Fragments.

Amides containing two norbornene fragments. Synthesis and chemical transformations

Kas?yan L. I., Isaev A. K., Kas?yan A. O., Golodaeva E. A., Karpenko D. V., Tarabara I. N.

Russian Journal of Organic Chemistry, 40, 1415–1426 (2004)

Amides containing two norbornene fragments. Synthesis and chemical transformations.

Reaction of Endic Anhydride with Hydrazines and Acylhydrazines

Krishchik O. V., Tarabara I. N., Kas’yan A. O., Shishkina S. V., Shishkin O. V., Isaev A. K., Kas’yan L.

Russian Journal of Organic Chemistry, 40, 1140–1145 (2004)

Reaction of Endic Anhydride with Hydrazines and Acylhydrazines.

Modeling the Gas-Phase Reduction of Nitrobenzene to Nitrosobenzene by Iron Monoxide: A Density Functional Theory Study

Zilberberg I., Ilchenko M., Isayev O., Gorb L., Leszczynski J.

The Journal of Physical Chemistry A, 108, 4878–4886 (2004)

Modeling the Gas-Phase Reduction of Nitrobenzene to Nitrosobenzene by Iron Monoxide: A Density Functional Theory Study.

Amino Alcohols with Bicyclic Carbon Skeleton. Alternative Functionalization of Nucleophilic Reaction Centers

Kas’yan L. I., Golodaeva E. A., Kas’yan A. O., Isaev A. K.

Russian Journal of Organic Chemistry, 39, 1398–1405 (2003)

Amino Alcohols with Bicyclic Carbon Skeleton. Alternative Functionalization of Nucleophilic Reaction Centers.

New N-(Arylsulfonyl)-5-aminomethylbicyclo[2.2.1]hept-2-enes. Synthesis, 1H and 13C NMR Spectra, and Chemical Reactions

Kas’yan A. O., Isaev A. K., Kas’yan L. I.

Russian Journal of Organic Chemistry, 38, 553–563 (2002)

New N-(Arylsulfonyl)-5-aminomethylbicyclo[2.2.1]hept-2-enes. Synthesis, 1H and 13C NMR Spectra, and Chemical Reactions.