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Combined usage of ligand- and structure-based virtual screening in the artificial intelligence era
European Journal of Medicinal Chemistry ( IF 6.0 ) Pub Date : 2024-12-10 , DOI: 10.1016/j.ejmech.2024.117162
Jingyi Dai, Ziyi Zhou, Yanru Zhao, Fanjing Kong, Zhenwei Zhai, Zhishan Zhu, Jie Cai, Sha Huang, Ying Xu, Tao Sun

Drug design has always been pursuing techniques with time- and cost-benefits. Virtual screening, generally classified as ligand-based (LBVS) and structure-based (SBVS) approaches, could identify active compounds in the large chemical library to reduce time and cost. Owing to the intrinsic flaws and complementary nature of both approaches, continued efforts have been made to combine them to mitigate limitations. Meanwhile, the emergence of machine learning (ML) endows them with opportunities to leverage vast amounts of data to improve their defects. However, few discussions on how to merge ML-improved LBVS and SBVS have been conducted. Therefore, this review provides insights into combined usage of ML-improved LBVS and SBVS to enlighten medicinal chemists to utilize these joint strategies to lift the screening efficiency as well as AI professionals to design novel techniques.

中文翻译:


人工智能时代基于配体和基于结构的虚拟筛选的联合使用



药物设计一直在追求具有时间和成本效益的技术。虚拟筛选通常分为基于配体 (LBVS) 和基于结构 (SBVS) 的方法,可以在大型化学库中识别活性化合物,以减少时间和成本。由于这两种方法的内在缺陷和互补性,人们一直在努力将它们结合起来以减轻限制。同时,机器学习 (ML) 的出现为他们提供了利用大量数据来改进缺陷的机会。然而,关于如何合并 ML 改进的 LBVS 和 SBVS 的讨论很少。因此,本综述提供了对 ML 改进的 LBVS 和 SBVS 联合使用的见解,以启发药物化学家利用这些联合策略来提高筛选效率,以及人工智能专业人员设计新技术。
更新日期:2024-12-10
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