Laboratory of Fundamental and Applied Research of Quality and Technology of Food Production, Moscow State University of Food Production, Volokolamskoe Shosse 11, 125080, Moscow, Russian Federation.
Department of Chemistry, Institute of Pharmacy, I.M. Sechenov First Moscow State Medical University, Trubetskaya Str., 8, b. 2, 119992, Moscow, Russian Federation.
Mini Rev Med Chem. 2020;20(14):1357-1374. doi: 10.2174/1389557520666200204123156.
Fragment-Based Drug Design (FBDD) has established itself as a promising approach in modern drug discovery, accelerating and improving lead optimization, while playing a crucial role in diminishing the high attrition rates at all stages in the drug development process. On the other hand, FBDD has benefited from the application of computational methodologies, where the models derived from the Quantitative Structure-Activity Relationships (QSAR) have become consolidated tools. This mini-review focuses on the evolution and main applications of the QSAR paradigm in the context of FBDD in the last five years. This report places particular emphasis on the QSAR models derived from fragment-based topological approaches to extract physicochemical and/or structural information, allowing to design potentially novel mono- or multi-target inhibitors from relatively large and heterogeneous databases. Here, we also discuss the need to apply multi-scale modeling, to exemplify how different datasets based on target inhibition can be simultaneously integrated and predicted together with other relevant endpoints such as the biological activity against non-biomolecular targets, as well as in vitro and in vivo toxicity and pharmacokinetic properties. In this context, seminal papers are briefly analyzed. As huge amounts of data continue to accumulate in the domains of the chemical, biological and biomedical sciences, it has become clear that drug discovery must be viewed as a multi-scale optimization process. An ideal multi-scale approach should integrate diverse chemical and biological data and also serve as a knowledge generator, enabling the design of potentially optimal chemicals that may become therapeutic agents.
基于片段的药物设计（FBDD）已经成为现代药物发现中一种很有前途的方法，它可以加速和改善先导化合物的优化，同时在减少药物开发过程各个阶段的高淘汰率方面发挥着关键作用。另一方面，FBDD得益于计算方法的应用，其中定量构效关系（QSAR）得出的模型已经成为了一种可靠的工具。这篇迷你综述重点介绍了在过去五年中，QSAR 范式在 FBDD 中的发展和主要应用。本报告特别强调了基于片段的拓扑方法得出的 QSAR 模型，这些模型可以提取物理化学和/或结构信息，从而可以从相对较大且异构的数据库中设计潜在的新型单靶点或多靶点抑制剂。在这里，我们还讨论了应用多尺度建模的必要性，以举例说明如何同时整合和预测基于靶标抑制的不同数据集，以及其他相关终点，如对非生物靶标的生物学活性、体外和体内毒性以及药代动力学特性。在这方面，简要分析了一些开创性的论文。随着化学、生物和生物医学领域的数据不断积累，很明显，药物发现必须被视为一个多尺度优化过程。理想的多尺度方法应该整合不同的化学和生物学数据，并作为知识生成器，从而能够设计出可能成为治疗剂的潜在最佳化学物质。
