INTRODUCTION Structure-based drug design is a wide area of identification of selective inhibitors of a target of interest. From the time of the availability of three dimensional structure of the drug targets, mostly the proteins, many computational methods had emerged to address the challenges associated with drug design process. Particularly, drug-likeness, druggability of the target protein, specificity, off-target binding, etc., are the important factors to determine the efficacy of new chemical inhibitors. OBJECTIVE The aim of the present research was to improve the drug design strategies in field of design of novel inhibitors with respect to specific target protein in disease pathology. Recent statistical machine learning methods applied for structural and chemical data analysis had been elaborated in current drug design field. METHODS As the size of the biological data shows a continuous growth, new computational algorithms and analytical methods are being developed with different objectives. It covers a wide area, from protein structure prediction to drug toxicity prediction. Moreover, the computational methods are available to analyze the structural data of varying types and sizes of which, most of the semi-empirical force field and quantum mechanics based molecular modeling methods showed a proven accuracy towards analysing small structural data sets while statistics based methods such as machine learning, QSAR and other specific data analytics methods are robust for large scale data analysis. RESULTS In this present study, the background has been reviewed for new drug lead development with respect specific drug targets of interest. Overall approach of both the extreme methods were also used to demonstrate with the plausible outcome. CONCLUSION In this chapter, we focus on the recent developments in the structure-based drug design using advanced molecular modeling techniques in conjunction with machine learning and other data analytics methods. Natural products based drug discovery is also discussed.

中文翻译：

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药物设计和发现的最新趋势。

引言基于结构的药物设计是广泛鉴定目标靶标选择性抑制剂的领域。从可获得药物靶标的三维结构（主要是蛋白质）开始，出现了许多计算方法来应对与药物设计过程相关的挑战。特别地，药物的相似性，靶蛋白的可药性，特异性，脱靶结合等是确定新型化学抑制剂功效的重要因素。目的本研究的目的是针对疾病病理学中的特定靶蛋白，改进新型抑制剂设计领域的药物设计策略。在当前的药物设计领域，已经阐述了用于结构和化学数据分析的最新统计机器学习方法。方法随着生物数据量的不断增长，正在针对不同目标开发新的计算算法和分析方法。它涵盖了从蛋白质结构预测到药物毒性预测的广泛领域。而且，计算方法可用于分析不同类型和大小的结构数据，其中大多数基于半经验力场和量子力学的分子建模方法在分析小型结构数据集方面显示出了公认的准确性，而基于统计的方法随着机器学习的发展，QSAR和其他特定的数据分析方法对于大规模数据分析是强大的。结果在本研究中，就感兴趣的特定药物靶标对新药铅开发的背景进行了综述。两种极端方法的整体方法也被用来证明可能的结果。结论在本章中，我们重点介绍使用先进分子建模技术结合机器学习和其他数据分析方法的基于结构的药物设计的最新进展。还讨论了基于天然产物的药物发现。