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arXiv · 2212.13617

SVSBI: Sequence-based virtual screening of biomolecular interactions

Li Shen·Hongsong Feng·Yuchi Qiu·Guo-Wei Wei
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Abstract

Virtual screening (VS) is an essential technique for understanding biomolecular interactions, particularly, drug design and discovery. The best-performing VS models depend vitally on three-dimensional (3D) structures, which are not available in general but can be obtained from molecular docking. However, current docking accuracy is relatively low, rendering unreliable VS models. We introduce sequence-based virtual screening (SVS) as a new generation of VS models for modeling biomolecular interactions. The SVS model utilizes advanced natural language processing (NLP) algorithms and optimizes deep $K$-embedding strategies to encode biomolecular interactions without invoking 3D structure-based docking. We demonstrate the state-of-art performance of SVS for four regression datasets involving protein-ligand binding, protein-protein, protein-nucleic acid binding, and ligand inhibition of protein-protein interactions and five classification datasets for the protein-protein interactions in five biological species. SVS has the potential to dramatically change the current practice in drug discovery and protein engineering.

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Li Shen, Hongsong Feng, Yuchi Qiu, Guo-Wei Wei. 2022-12-27. SVSBI: Sequence-based virtual screening of biomolecular interactions. https://arxiv.org/abs/2212.13617

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