deep learning

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.

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)

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.

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.

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.

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.

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.

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.

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.

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.

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.