GenShin:geometry-enhanced structural graph embodies binding pose can better predicting compound-protein interaction affinity

• URL: http://arxiv.org/abs/2504.13853v1
• Date: Sun, 16 Mar 2025 09:11:56 GMT
• Title: GenShin:geometry-enhanced structural graph embodies binding pose can better predicting compound-protein interaction affinity
• Authors: Pingfei Zhu, Chenyang Zhao, Haishi Zhao, Bo Yang,
• Abstract summary: We introduce the GenShin model, which constructs a geometry-enhanced structural graph module that extracts additional features from proteins and compounds.<n>It attains an accuracy on par with mainstream models in predicting compound-protein affinities, while eliminating the need for adequate-binding pose as input.<n>Our work will inspire more endeavors to bridge the gap between AI models and practical drug discovery challenges.
• Score: 6.1468096893238915
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: AI-powered drug discovery typically relies on the successful prediction of compound-protein interactions, which are pivotal for the evaluation of designed compound molecules in structure-based drug design and represent a core challenge in the field. However, accurately predicting compound-protein affinity via regression models usually requires adequate-binding pose, which are derived from costly and complex experimental methods or time-consuming simulations with docking software. In response, we have introduced the GenShin model, which constructs a geometry-enhanced structural graph module that separately extracts additional features from proteins and compounds. Consequently, it attains an accuracy on par with mainstream models in predicting compound-protein affinities, while eliminating the need for adequate-binding pose as input. Our experimental findings demonstrate that the GenShin model vastly outperforms other models that rely on non-input docking conformations, achieving, or in some cases even exceeding, the performance of those requiring adequate-binding pose. Further experiments indicate that our GenShin model is more robust to inadequate-binding pose, affirming its higher suitability for real-world drug discovery scenarios. We hope our work will inspire more endeavors to bridge the gap between AI models and practical drug discovery challenges.

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