Abstract：
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. In-silico 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, in-silico tools, virtual screening in drug discovery, and the concept of natural products as anticancer therapies. Representative examples of molecules identified will also be provided.
摘要：
天然植物来源在几种抗癌药物的开发中是必不可少的，比如长春新碱,长春碱,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,各种化疗由于不良反应而失败，耐药性,和目标特异性。研究人员现在专注于开发使用天然化合物来克服这些问题的药物。这些药物可以影响多个目标，减少了不良影响，并且对几种癌症都有效。开发一种新药是非常复杂的，贵,和耗时的过程。传统的药物发现方法需要长达15年的时间才能使新药进入市场，成本超过10亿美元。然而,最近的计算机辅助药物发现（CADD）的进步改变了这种情况。本文旨在全面描述从天然产物中鉴定抗癌药物的不同CADD方法。各种来源的数据，包括科学直接，Elsevier,NCBI,和WebofScience,在这篇评论中使用。计算机技术和优化算法可以在药物发现风险中提供通用的解决方案。基于结构的药物设计技术被广泛用于了解化学成分的分子水平相互作用和识别命中线索。这篇综述将讨论CADD的概念，在硅工具，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。还将提供鉴定的分子的代表性实例。
