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原文連結
論文資訊
- 類型：已發表論文
- 日期：2023-04-08
摘要
Identifying novel drug-target interactions is a critical and rate-limiting step in drug discovery. While 深度學習 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 蛋白質-ligand bipartite 網絡, rather than learning the node features. Here we introduce AI-Bind, a pipeline that combines 網絡-based sampling strategies with unsupervised pre-training to improve binding predictions for novel 蛋白質s and ligands. We validate AI-Bind predictions via docking simulations and comparison with recent experimental evidence, and step up the process of interpret
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