The Isayev lab works at the interface of theoretical chemistry, pharmaceutical sciences and computer science. In particular, we are using molecular simulations and artificial intelligence (AI) to solve hard problems in chemistry. We are working towards the acceleration of molecular discovery by the combination of AI, informatics and high-throughput quantum chemistry. We also focus on both generative and predictive ML models for chemical and biological data. Details on specific projects can be found below.
Accelerating computational chemistry with deep learning: We are developing fully transferable deep learning potentials for molecular and materials systems. Such atomistic potentials are highly accurate compared to reference QM calculations at speeds 107faster. Neural network potentials are shown to accurately represent the underlying physical chemistry of molecules through various test cases including chemical reactions, kinetics, thermochemistry, structural optimization, and molecular dynamics simulations.
Materials informatics: Material informatics is a rapidly emerging data- and knowledge-driven approach for the identification of novel materials for a range of applications, including solar energy conversion. As the proliferation of high-throughput methods in chemical sciences is increasing the wealth of data in the field, the gap between accumulated-information and derived knowledge widens. We address the issue of scientific discovery in chemical and biological databases by introducing novel analytical approaches based on large-scale data mining and machine learning.
De Novo molecular design: The de novo molecular design problem involves generating novel molecular structures or focused molecular libraries with desirable properties. It solves a so-called inverse design problem. We develop artificial intelligence method that enables the design of chemical libraries with the desired physicochemical and biological properties or both.
|Gokcan, Hatice||Postdoctoral Fellowemail@example.com|
|Gusev, Phil||Graduate Studentfirstname.lastname@example.org|
|Liu, Zhen (Jack)||Graduate Studentemail@example.com|
|Zhang, Shuhao||Graduate Studentfirstname.lastname@example.org|
- "Machine learning for molecular and materials science," Keith T Butler, Daniel W Davies, Hugh Cartwright, Olexandr Isayev, Aron Walsh. Nature, 559, 547-555 (2018)
- "ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost," Justin S Smith, Olexandr Isayev, Adrian E Roitberg. Chemical science, 8, 3192-3203 (2017)
- "Universal fragment descriptors for predicting properties of inorganic crystals," Olexandr Isayev, Corey Oses, Cormac Toher, Eric Gossett, Stefano Curtarolo, Alexander Tropsha. Nature communications, 8, 1-12 (2017)
- "Materials cartography: representing and mining materials space using structural and electronic fingerprints," Olexandr Isayev, Denis Fourches, Eugene N Muratov, Corey Oses, Kevin Rasch, Alexander Tropsha, Stefano Curtarolo. Chemistry of Materials, 27, 735-743 (2015)
- "Deep reinforcement learning for de novo drug design," Mariya Popova, Olexandr Isayev, Alexander Tropsha. Science advances, 4, eaap7885 (2018)
- "Crowdsourced mapping of unexplored target space of kinase inhibitors," Anna Cichonska, Balaguru Ravikumar, Robert J Allaway, Sungjoon Park, Fangping Wan, Olexandr Isayev, Shuya Li, Michael J Mason, Andrew Lamb, Minji Jeon, Sunkyu Kim, Mariya Popova, Jianyang Zeng, Kristen Dang, Gregory Koytiger, Jaewoo Kang, Carrow I Wells, Timothy M Willson, Tudor I Oprea, Avner Schlessinger, David H Drewry, Gustavo A Stolovitzky, Krister Wennerberg, Justin Guinney, Tero Aittokallio. bioRxiv, (2020)
- "Predicting Thermal Properties of Crystals Using Machine Learning," Sherif Abdulkader Tawfik, Olexandr Isayev, Michelle JS Spencer, David A Winkler. Advanced Theory and Simulations, 1900208, (2019)
- "Impressive computational acceleration by using machine learning for 2-dimensional super-lubricant materials discovery," Marco Fronzi, Mutaz Abu Ghazaleh, Olexandr Isayev, David A Winkler, Joe Shapter, Michael J Ford. arXiv preprint arXiv, 1911.11559 (2019)
- "The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules," Justin S Smith, Roman Zubatyuk, Benjamin T Nebgen, Nicholas Lubbers, Kipton Barros, Adrian Roitberg, Olexandr Isayev, Sergei Tretiak. ChemRxiv (2019)
- "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, 35, 3584-3591, (2019)