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Abstract: Proximity-inducing compounds (PICs) are an emergent drug technology through which the protein of interest (POI) is brought into the vicinity of proteins that control various cellular processes, giving rise to therapeutic benefits. One of the best-known PICs examples are heterobifunctional molecules known as proteolysis targeting chimeras (PROTACs), which induce protein degradation by establishing proximity between a POI and an E3 ligase. In silico PROTAC discovery requires computationally predicting the ternary complex consisting of POI, PROTAC molecule, and E3 ligase. To date, however, all of the approaches for modeling ternary complexes have not been both effective and computationally fast enough. We present a novel machine learning-based method for predicting PROTAC-mediated ternary complex structures based on Bayesian optimization. We show how a fitness combining an estimation of protein-protein interactions with PROTAC energy allows to find good candidate structures...

Speaker: Noah Weber

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