Low Data Drug Discovery with One-Shot Learning

Date: April 18, 2pm

Speaker: Bharath Ramsundar

Location: Berryessa Conference Room (B53-2002)  (NOTE DIFFERENT ROOM!)

Abstract: Recent advances in machine learning have made significant contributions to drug discovery. Deep neural networks in particular have been demonstrated to provide significant boosts in predictive power when inferring the properties and activities of small-molecule compounds. However, the applicability of deep learning has been limited by the requirement for large amounts of training data. In this work, we demonstrate how one-shot learning can be used to significantly lower the amounts of data required to make meaningful predictions in drug discovery applications. We introduce a new architecture, the iterative refinement long short-term memory, that, when combined with graph convolutional neural networks, significantly improves learning of meaningful distance metrics over small-molecules. Our models are open-sourced as part of DeepChem, an open framework for deep-learning in drug discovery and quantum chemistry.

 

Bio: Bharath Ramsundar received a BA and BS from UC Berkeley in EECS and Mathematics and was valedictorian of his graduating class in mathematics. He is currently a PhD student in computer science at Stanford University with the Pande group. His research focuses on the application of deep-learning to drug-discovery. In particular, Bharath is the creator and lead-developer of DeepChem, an open source package that aims to democratize the use of deep-learning in drug-discovery and quantum chemistry. He is supported by a Hertz Fellowship, the most selective graduate fellowship in the sciences.

 

Machine Learning at NERSC: Strategy, Tools and Applications

 

Date: May 9, 2pm

 

Speaker: Prabhat (NERSC)