Genentech: David Graff | Senior ML Scientist, Computational Sciences

Scientists / Our Scientists

I originally received my bachelor’s in chemistry from Princeton in 2018 with a focus in synthetic organic chemistry and then went on to graduate school to continue those studies. Shortly after starting, I made the transition to computational chemistry and worked on developing optimization methods for accelerated virtual screening. At Genentech, I work on researching new techniques and building tools for small molecule drug discovery using machine learning and computation.

Featured Publication

Accelerating high-throughput virtual screening through molecular pool-based active learning.

Chem Sci. 2021

Graff, D. E.; Shakhnovich, E.I.; and Coley, C.W.

View Abstract

I’m a member of the small molecule drug discovery team inside Prescient Design. In this role, I work on translating recent advances in machine learning (ML) and AI to active drug discovery programs. This involves not just training ML models to generate molecules and predict their properties but also developing new algorithms to adapt these techniques to real-world constraints, such as molecular synthesizability and the ultra-large scales typical of virtual screening. A large part of this work involves designing the software systems that enable chemists to apply these approaches in their own discovery projects.

Harvard University, Ph. D. in Chemistry – 2023
Princeton University, B.A. in Chemistry – 2018

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David Graff

Senior ML Scientist, Computational Sciences

Featured Publication

Accelerating high-throughput virtual screening through molecular pool-based active learning.