Data-driven Modeling Approaches in Computational Drug Discovery

Abstract: 

This talk will detail the specifics about issues around building predictive models for small molecule drug discovery focusing first on target specific drug modeling approaches and then on structure based multi-target modeling approaches.

Bio: 

Hiranmayi is a machine learning specialist at Accelerating Therapeutics for Opportunities in Medicine (ATOM). As part of the data modeling team, she works on building deep learning models of secondary pharmacology, with the goal of predicting adverse effects of drug candidates before they advance to animal and human trials. She joined Lawrence Livermore National Laboratory (LLNL) in July 2019 and has been part of the machine learning group since then. Before that, she did her Ph.D. in Electrical Engineering from Arizona State University. Her research interests are in Deep Learning, Active Learning, Emotion Recognition, and Deep Learning for drug discovery.

Open Data Science

 

 

 

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