Predicting Protein-Ligand Binding Affinity via Joint Global-Local Interaction Modeling

• URL: http://arxiv.org/abs/2209.13014v1
• Date: Sun, 18 Sep 2022 10:17:05 GMT
• Title: Predicting Protein-Ligand Binding Affinity via Joint Global-Local Interaction Modeling
• Abstract summary: We propose a novel global-local interaction framework to predict protein-ligand binding affinity. Our framework embeds the long-range interactions globally and aggregates local short-range interactions, respectively. Experiments demonstrate that our GLI framework outperforms state-of-the-art methods with simple neural network architectures and moderate computational costs.
• Score: 34.18714126231236
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The prediction of protein-ligand binding affinity is of great significance for discovering lead compounds in drug research. Facing this challenging task, most existing prediction methods rely on the topological and/or spatial structure of molecules and the local interactions while ignoring the multi-level inter-molecular interactions between proteins and ligands, which often lead to sub-optimal performance. To solve this issue, we propose a novel global-local interaction (GLI) framework to predict protein-ligand binding affinity. In particular, our GLI framework considers the inter-molecular interactions between proteins and ligands, which involve not only the high-energy short-range interactions between closed atoms but also the low-energy long-range interactions between non-bonded atoms. For each pair of protein and ligand, our GLI embeds the long-range interactions globally and aggregates local short-range interactions, respectively. Such a joint global-local interaction modeling strategy helps to improve prediction accuracy, and the whole framework is compatible with various neural network-based modules. Experiments demonstrate that our GLI framework outperforms state-of-the-art methods with simple neural network architectures and moderate computational costs.

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