| Autor: |
Chatterjee, Ayan, Walters, Robin, Shafi, Zohair, Ahmed, Omair Shafi, Sebek, Michael, Gysi, Deisy, Yu, Rose, Eliassi-Rad, Tina, Barabási, Albert-László, Menichetti, Giulia |
| Předmět: |
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| Zdroj: |
Nature Communications; 4/8/2023, Vol. 14 Issue 1, p1-15, 15p |
| Abstrakt: |
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. State-of-the-art machine learning models in drug discovery fail to reliably predict the binding properties of poorly annotated proteins and small molecules. Here, the authors present AI-Bind, a machine learning pipeline to improve generalizability and interpretability of binding predictions. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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