Abstract (may include machine translation)
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.
Original language | English |
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Article number | 1989 |
Pages (from-to) | 1989 |
Journal | Nature Communications |
Volume | 14 |
Issue number | 1 |
DOIs | |
State | Published - 8 Apr 2023 |
Keywords
- Amino Acid Sequence
- Binding Sites
- Ligands
- Protein Binding
- Proteins/metabolism