Improving the generalizability of protein-ligand binding predictions with AI-Bind

Ayan Chatterjee, Robin Walters, Zohair Shafi, Omair Shafi Ahmed, Michael Sebek, Deisy Gysi, Rose Yu, Tina Eliassi-Rad, Albert-László Barabási and Giulia Menichetti ()
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Ayan Chatterjee: Northeastern University
Robin Walters: Northeastern University
Zohair Shafi: Northeastern University
Omair Shafi Ahmed: Northeastern University
Michael Sebek: Northeastern University
Deisy Gysi: Northeastern University
Rose Yu: University of California
Tina Eliassi-Rad: Northeastern University
Albert-László Barabási: Northeastern University
Giulia Menichetti: Northeastern University

Nature Communications, 2023, vol. 14, issue 1, 1-15

Abstract: Abstract Identifying novel drug-target interactions is a critical and rate-limiting step in drug discovery. While deep learning models have been proposed to accelerate the identification process, here we show that state-of-the-art models fail to generalize to novel (i.e., never-before-seen) structures. We unveil the mechanisms responsible for this shortcoming, demonstrating how models rely on shortcuts that leverage the topology of the protein-ligand bipartite network, rather than learning the node features. Here we introduce AI-Bind, a pipeline that combines network-based sampling strategies with unsupervised pre-training to improve binding predictions for novel proteins and ligands. We validate AI-Bind predictions via docking simulations and comparison with recent experimental evidence, and step up the process of interpreting machine learning prediction of protein-ligand binding by identifying potential active binding sites on the amino acid sequence. AI-Bind is a high-throughput approach to identify drug-target combinations with the potential of becoming a powerful tool in drug discovery.

Date: 2023
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DOI: 10.1038/s41467-023-37572-z

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