Learning physics confers pose-sensitivity in structure-based virtual screening

By P. Gniewek, Bradley Worley, K. Stafford, H. V. D. Bedem, Brandon M. Anderson. 2021

10 citations recorded by the source corpus.

In drug discovery, structure-based virtual high-throughput screening (vHTS) campaigns aim to identify bioactive ligands or"hits"for therapeutic protein targets from docked poses at specific binding sites. However, while generally successful at this task, many deep learning methods are known to be insensitive to protein-ligand interactions, decreasing the reliability of hit detection and hindering discovery at novel binding sites. Here, we overcome this limitation by introducing a class of models with two key features: 1) we condition bioactivity on pose quality score, and 2) we present poor poses of true binders to the model as negative examples. The conditioning forces the model to learn details of physical interactions. We evaluate these models on a new benchmark designed to detect pose-sensitivity.

Subfields: Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction (2022, methodology): “, fast virtual screening (Gniewek et al., 2021; Jastrzebski et al., 2020) or de novo binder generation (Masuda et al.”