Computational Drug Discovery & Deep Learning - Olexandr Isayev, University of North Carolina

Computational drug discovery is an effective strategy for accelerating drug discovery and development process. Because of the dramatic growth of biological and small molecule data, the applicability of computational methods like data mining, machine learning has been extended and broadly applied to nearly every stage in the drug discovery and development workflow. Over the past year, computational drug discovery with Deep learning (DL) is rapidly emerging. DL allows for multi-task learning, the procedure of learning several tasks at the same time with the aim of mutual benefits. DL can also be exploited for representation learning and dimensionality reduction. In this talk, I present an overview of these developments, platforms and early successful applications in our lab as well as in the field.

Olexandr Isayev is a Research Scientist at UNC Eshelman School of Pharmacy, the University of North Carolina at Chapel Hill. In 2008, Olexandr received his PhD in computational chemistry. He was Postdoctoral Research Fellow at the Case Western Reserve University and scientist at the government research lab before joining UNC in 2013. Olexandr received “Emerging Technology Award” from the American Chemical Society (ACS) and the GPU computing award from NVIDIA in 2014. His research interests focus on making sense of chemical data with molecular modelling and machine learning.

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