AI-Bind: Improving Binding Predictions for Novel Protein Targets and Ligands

• URL: http://arxiv.org/abs/2112.13168v2
• Date: Tue, 28 Dec 2021 05:29:29 GMT
• Title: AI-Bind: Improving Binding Predictions for Novel Protein Targets and Ligands
• Authors: Ayan Chatterjee, Omair Shafi Ahmed, Robin Walters, Zohair Shafi, Deisy Gysi, Rose Yu, Tina Eliassi-Rad, Albert-L\'aszl\'o Barab\'asi and Giulia Menichetti
• Abstract summary: We show that state-of-the-art models fail to generalize to novel structures. We introduce AI-Bind, a pipeline that combines network-based sampling strategies with unsupervised pre-training. We illustrate the value of AI-Bind by predicting drugs and natural compounds with binding affinity to SARS-CoV-2 viral proteins.
• Score: 9.135203550164833
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Identifying novel drug-target interactions (DTI) is a critical and rate limiting step in drug discovery. While deep learning models have been proposed to accelerate the identification process, we show that state-of-the-art models fail to generalize to novel (i.e., never-before-seen) structures. We first 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. Then, we introduce AI-Bind, a pipeline that combines network-based sampling strategies with unsupervised pre-training, allowing us to limit the annotation imbalance and improve binding predictions for novel proteins and ligands. We illustrate the value of AI-Bind by predicting drugs and natural compounds with binding affinity to SARS-CoV-2 viral proteins and the associated human proteins. We also validate these predictions via auto-docking simulations and comparison with recent experimental evidence. Overall, AI-Bind offers a powerful high-throughput approach to identify drug-target combinations, with the potential of becoming a powerful tool in drug discovery.

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