SVSBI：基于序列的生物分子相互作用的虚拟筛选
SVSBI: Sequence-based virtual screening of biomolecular interactions
摘要：
虚拟筛选（VS）是理解生物分子相互作用，特别是药物设计和发现的重要技术。性能最好的VS模型主要依赖于三维（3D）结构，这是一般情况下不可用，但可以从分子对接。然而，目前的对接精度相对较低，导致不可靠的VS模型。我们介绍了基于序列的虚拟筛选（SVS）作为新一代的VS模型建模生物分子相互作用。SVS模型利用先进的自然语言处理（NLP）算法并优化深度$K$嵌入策略来编码生物分子相互作用，而无需调用基于3D结构的对接。我们证明了SVS的四个回归数据集，涉及蛋白质-配体结合，蛋白质-蛋白质，蛋白质-核酸结合，配体抑制蛋白质相互作用和五个分类数据集的蛋白质相互作用在五个生物物种的最先进的性能。SVS有可能极大地改变目前药物发现和蛋白质工程的实践。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
