Priya Muthiah Gnana Ruba, Manisha Jessica, Lazar Lal Prasanth Mercy, Rathore Seema Singh, Solomon Viswas Raja
College of Pharmaceutical Sciences, Department of Pharmaceutical Chemistry, Dayananda Sagar University, Bangalore, Karnataka, India.
Department of Pharmacology, Sridevi College of Pharmacy, Rajiv Gandhi University of Health Sciences, Bangalore, Karnataka, India.
Curr Comput Aided Drug Des. 2025;21(1):1-14. doi: 10.2174/0115734099283410240406064042.
Natural plant sources are essential in the development of several anticancer drugs, such as vincristine, vinblastine, vinorelbine, docetaxel, paclitaxel, camptothecin, etoposide, and teniposide. However, various chemotherapies fail due to adverse reactions, drug resistance, and target specificity. Researchers are now focusing on developing drugs that use natural compounds to overcome these issues. These drugs can affect multiple targets, have reduced adverse effects, and are effective against several cancer types. Developing a new drug is a highly complex, expensive, and time-consuming process. Traditional drug discovery methods take up to 15 years for a new medicine to enter the market and cost more than one billion USD. However, recent Computer Aided Drug Discovery (CADD) advancements have changed this situation. This paper aims to comprehensively describe the different CADD approaches in identifying anticancer drugs from natural products. Data from various sources, including Science Direct, Elsevier, NCBI, and Web of Science, are used in this review. techniques and optimization algorithms can provide versatile solutions in drug discovery ventures. The structure-based drug design technique is widely used to understand chemical constituents' molecular-level interactions and identify hit leads. This review will discuss the concept of CADD, tools, virtual screening in drug discovery, and the concept of natural products as anticancer therapies. Representative examples of molecules identified will also be provided.
天然植物来源在多种抗癌药物的研发中至关重要，比如长春新碱、长春花碱、长春瑞滨、多西他赛、紫杉醇、喜树碱、依托泊苷和替尼泊苷。然而，由于不良反应、耐药性和靶点特异性，各种化疗方法均告失败。研究人员目前正专注于开发利用天然化合物来克服这些问题的药物。这些药物可以作用于多个靶点，副作用较小，并且对多种癌症类型都有效。开发一种新药是一个极其复杂、昂贵且耗时的过程。传统的药物发现方法需要长达15年的时间新药才能进入市场，成本超过10亿美元。然而，最近计算机辅助药物发现（CADD）的进展改变了这种状况。本文旨在全面描述从天然产物中识别抗癌药物的不同CADD方法。本综述使用了来自各种来源的数据，包括科学Direct、爱思唯尔、NCBI和科学网。技术和优化算法可以在药物发现项目中提供多种解决方案。基于结构的药物设计技术被广泛用于理解化学成分的分子水平相互作用并识别有潜力的先导化合物。本综述将讨论CADD的概念、工具、药物发现中的虚拟筛选以及天然产物作为抗癌疗法的概念。还将提供已识别分子的代表性实例。
