"We are making models that combine strengths from both machine learning and molecular structure worlds, and hope that these models can power new generations of lab-in-the-loop drug discovery pipelines."
I combine biophysics and computational methods to build new approaches to biomolecular design. I have a background in computational structural biology. As a postdoc I became more involved in genomics data analysis and this led to a deep specialisation in the inference and modeling of biological networks. My group now combines machine learning approaches with physical models of key biological processes to make progress on molecular and biomolecular design.
The Postdoc program at Genentech has always been a foundry for innovation in biotech, and I feel quite lucky to be able to participate. Postdoctoral Fellows at Prescient will help us push boundaries in ML, f unify approaches, and generally think big. The postdoc program at Genentech helps comprise our ‘basic science’ core and will also interface with our Frontiers group and other ML focused efforts across the Roche family.
Prescient Design is a gRED accelerator focused on developing machine learning methods for drug discovery. In particular we are focused on biomolecular structure, and structure-function relationships that underpin drug discovery. Our work integrates structure biology approaches, bioinformatics/chemoinformatics, and natural language processing to derive new hybrid methods for design.