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Mathematical Biology Seminar

October 5, 2022 - 10:00am

Room: ACS 362C
Speaker: Dr Jonathan Allen (LLNL)
Title: Challenges and opportunities in data driven drug discovery
Abstract: Advances in chemical synthesis are expanding the number of molecules that can be easily made and tested for drug discovery. The exceptionally large ‘makeable’ chemical space means experimental data is collected for a tiny fraction of candidate molecules and this fundamentally limits the molecules found using an experimentally driven drug discovery process. Computational search of the virtual chemical space has the potential to find molecules that meet multiple design criteria and increase the chances of a drug candidate advancing to human clinical trials. This talk will introduce elements of the Accelerating Therapeutics for Opportunities in Medicine (ATOM) drug discovery framework including a generative statistical chemical model and an iterative drug design loop that selectively explores chemical space using data-driven small molecule property prediction models and physics-based scoring functions. Preliminary results show the promise of an iterative design loop, with opportunities for improvement in areas such as quantifying model prediction uncertainty and optimizing chemical search techniques. The aim of this work is to build an open computational framework accessible to the broader research community that can improve the efficiency of drug discovery on new disease targets.