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EVENT DETAILS |
Quantitative Drug Discovery - Opportunities for Modeling & Simulation in the Age of Machine Learning
In my talk, I will discuss how different modeling approaches from machine learning to mechanistic computational models are applied throughout the drug discovery process. We discuss a proliferation screen across 58 molecularly characterized cell lines stimulated with multiple ligands. At the example of this data-set, we explore the potential to combine machine learning & mechanistic computational models to predict which cell lines proliferate in the presence of exogenous ligand. We show that the predictive model performance increases when simulated features derived from the mechanistic model are included as features into a Bagged Decision Tree (BDTs) model. The presented novel approach of using BDTs in conjunction with simulated signaling features is an example of how complex mechanistic models & large data sets can be combined to understand cellular complexity.
Dr. Birgit Schoeberl currently serves a Global Head of Modeling & Simulation, PK Sciences at Novartis. Before joining Novartis, Birgit served as interim Senior Vice President, Scientific Value, at GNS Healthcare. During her time at GNS, she focused on applying causal machine learning to drug development. Prior to GNS, she was a member of the founding team at Merrimack Pharmaceuticals & where she spent over 12 years in different roles. In her latest role as Head of Discovery & Early Development, she gained broad experience in oncology drug discovery & development. Birgit & her leadership team were responsible for multiple preclinical & early clinical programs.
Birgit received her Chemical Engineering degree from the Technical University in Karlsruhe, Germany, & her Ph.D. in Biological Engineering from the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg. She was a post-doctoral fellow in the laboratories of Douglas Lauffenburger & Peter Sorger in the Biological Engineering Department at MIT.
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