Department of Experimental Medicine and Biotechnology, Postgraduate Institute of Medical Education and Research, Sector-12, Chandigarh 160012, India.
Department of Computer Science, School of Electrical Engineering and Computer Sciences, KTH Royal Institute of Technology, S-100 44, Stockholm, Sweden; Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi 110020, India.
Drug Discov Today. 2022 Jul;27(7):1847-1861. doi: 10.1016/j.drudis.2022.03.006. Epub 2022 Mar 14.
The current global health emergency in the form of the Coronavirus 2019 (COVID-19) pandemic has highlighted the need for fast, accurate, and efficient drug discovery pipelines. Traditional drug discovery projects relying on in vitro high-throughput screening (HTS) involve large investments and sophisticated experimental set-ups, affordable only to big biopharmaceutical companies. In this scenario, application of efficient state-of-the-art computational methods and modern artificial intelligence (AI)-based algorithms for rapid screening of repurposable chemical space [approved drugs and natural products (NPs) with proven pharmacokinetic profiles] to identify the initial leads is a powerful option to save resources and time. Structure-based drug repurposing is a popular in silico repurposing approach. In this review, we discuss traditional and modern AI-based computational methods and tools applied at various stages for structure-based drug discovery (SBDD) pipelines. Additionally, we highlight the role of generative models in generating molecules with scaffolds from repurposable chemical space.
当前以 2019 年冠状病毒病（COVID-19）大流行形式出现的全球卫生紧急情况突出表明需要快速、准确和高效的药物发现管道。传统的药物发现项目依赖于体外高通量筛选（HTS），需要大量投资和复杂的实验设置，只有大型生物制药公司才能负担得起。在这种情况下，应用高效的最先进的计算方法和基于现代人工智能（AI）的算法来快速筛选可再利用的化学空间[具有经过验证的药代动力学特征的已批准药物和天然产物（NPs）]以确定初始先导物是节省资源和时间的有力选择。基于结构的药物重新利用是一种流行的计算药物重新利用方法。在这篇综述中，我们讨论了应用于基于结构的药物发现（SBDD）管道各个阶段的传统和现代基于 AI 的计算方法和工具。此外，我们还强调了生成模型在从可再利用的化学空间生成具有支架的分子方面的作用。
