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Improving the generalizability of protein-ligand binding predictions with AI-Bind
Title / Series / Name
Publication Volume
Publication Issue
Pages
Editors
Keywords
Amino Acid Sequence
Binding Sites
Ligands
Protein Binding
Proteins/metabolism
Binding Sites
Ligands
Protein Binding
Proteins/metabolism
URI
https://hdl.handle.net/20.500.14018/27126
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.
Topic
Publisher
Place of Publication
Type
Journal article
Date
2023-04-08
Language
ISBN
Identifiers
10.1038/s41467-023-37572-z