Barun Bhhatarai, Ph.D., Novartis Institutes for BioMedical Research

Barun Bhhatarai, Ph.D. Dr. Bhhatarai joined Novartis (NIBR) since Nov 2015 to support computational ADME projects using machine learning, data integration, ADME modeling and MedChem based approaches. Before, Novartis, he was a System Toxicology Informatics fellow in the Predictive Safety Assessment Center at The Dow Chemical Company, Michigan, where he contributed to the development, assessment and implementation of novel in-silico approaches aimed at predicting potential mammalian and environmental health effects. He did post-doctoral research focusing on cheminformatics, bio-assay/ chemical ontology and computer-aided drug design at Center for Computational Science at University of Miami, Florida. After obtaining his PhD from Clarkson University, New York in Dec 2007, he joined the University of Insubria, Varese, Italy where he performed Predictive Toxicology research and Risk Assessment of emerging pollutants for an EU-FP7 project - CADASTER. He is a member of the American Society for Cellular and Computational Toxicology (ASCCT) and the American Chemical Society (ACS - COMP, CINF). He has authored more than 25 peer-reviewed publications and several presentations.


12:05 PM - 12:45 PM

Opportunities and Challenges Using AI in unlocking the ADME Tox Obstacles in Drug Development

This session will address how machine and active learning are being applied to solve complex and difficult ADME Tox problems facing the pharma industry.  There will be representation from both in-house and external models as well as pro and con viewpoints on applying AI to the challenges of the ADME/tox field.  Industry innovators and researchers share their efforts in solving these challenges which could be worth hundreds of millions to industry, accelerate drug discovery and be invaluable to patients.
  • What problems are we trying to solve?
  • Do we have the right data to build ADME/Tox models?
  • How do we know when models will be applicable in a drug discovery program?
  • Is Deep Learning the solution to our problems?
  • How can we staff AI in drug discovery?



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