Hit Expansion Driven By Machine Learning

Recent work \cite{McCloskey2020-es} utilized experimental data from DNA-encoded library (DEL) selections to train graph convolutional neural networks (GCNNs) \cite{Kearnes2016-sk} for identifying hit compounds for protein targets and their prospective test results demonstrated excellent hit rates for three diverse proteins. Building on this work, we propose two novel approaches to leverage DEL GCNN model predictions and embeddings to automate hit expansion, a critical step in real-world drug discovery that guides the optimization of initial hit compounds toward clinical candidates. We prospectively tested the proposed approaches on a protein target (sEH) and our methods identified more small molecules with higher potency compared to traditional molecular fingerprint similarity searches. Specifically, we discovered $34$ molecules with higher potency than a sEH clinical trial candidate using our approaches. All sEH assay results are publicly available at \url{https://www.tdcommons.org/dpubs_series/7414/}. Furthermore, applying the automated hit expansion approach to WDR91, a novel protein target that has no known binders, led to the discovery of two first-in-class covalent binders that were experimentally confirmed by co-crystal structures.