Central University of South Bihar, Gaya, Bihar, India.
J Biomol Struct Dyn. 2024;42(23):12806-12821. doi: 10.1080/07391102.2023.2273437. Epub 2023 Oct 25.
New drug discovery is recognized as a complicated, costly, time-consuming, and difficult process. Computer-aided drug discovery has developed as a potent and promising method for faster, cheaper, and more effective drug creation. Recently, the rapid rise of computational methods for drug discovery, including anticancer medicines, had a substantial and exceptional impact on anticancer drug design, as well as providing beneficial insights into the field of cancer therapy. In this paper, we discussed the quantitative structure-activity relationship (QSAR), which is a significant in-silico tool in rational drug design. The QSAR method is used to optimize the existing leads to improve their biological activities, and physicochemical properties and to predict the biological activities of untested and sometimes unavailable compounds, so QSAR is a significant method in drug designing. This article is a comprehensive review of various QSAR studies conducted which help to create new and potent inhibitors for targeting tubulin, a crucial target in cancer treatment. It particularly focuses on studies that provide structural insights into the compounds targeting tubulin. It should prioritize continually researching specific scaffolds, with a focus on important attachment regions, to gather more powerful molecular data and enhance models. This will lead to a better understanding of drug interactions and the development of improved cancer-targeting inhibitors for tubulin.Communicated by Ramaswamy H. Sarma.
新药发现被认为是一个复杂、昂贵、耗时且困难的过程。计算机辅助药物发现已发展成为一种强大而有前途的方法,可以更快、更便宜、更有效地创造药物。最近,包括抗癌药物在内的药物发现计算方法的迅速兴起,对抗癌药物设计产生了重大而卓越的影响,并为癌症治疗领域提供了有益的见解。在本文中,我们讨论了定量构效关系(QSAR),这是合理药物设计中的一个重要计算工具。QSAR 方法用于优化现有先导化合物以提高其生物活性、物理化学性质,并预测未经测试且有时不可用的化合物的生物活性,因此 QSAR 是药物设计中的重要方法。本文是对各种 QSAR 研究的综合回顾,这些研究有助于为靶向微管蛋白的新型有效抑制剂的开发提供了帮助,微管蛋白是癌症治疗中的一个关键靶标。它特别关注提供针对微管蛋白的化合物的结构见解的研究。应该优先研究特定的支架,重点关注重要的附着区域,以收集更强大的分子数据并增强模型。这将有助于更好地理解药物相互作用,并开发出针对微管蛋白的改进的癌症靶向抑制剂。由 Ramaswamy H. Sarma 交流。