Université Paris Descartes, PRES Sorbonne Paris Cité, CNRS UMR 860, Laboratoire de Chimie et de Biochimie Pharmacologiques et Toxicologique, 45, Rue des Saints Peres, 75006 Paris, France; CQM - Centro de Química da Madeira, MMRG, Universidade da Madeira, Campus da Penteada, 9020-105 Funchal, Portugal; Medicinal Chemistry Division, Indian Institute of Integrative Medicine (Council of Scientific and Industrial Research), Canal Road, Jammu 180001, India.
CQM - Centro de Química da Madeira, MMRG, Universidade da Madeira, Campus da Penteada, 9020-105 Funchal, Portugal; School of Materials Science and Engineering/Center for Nano Energy Materials, Northwestern Polytechnical University, Xi'an, Shaanxi Province 710072, China.
Drug Discov Today. 2018 Mar;23(3):605-615. doi: 10.1016/j.drudis.2018.01.010. Epub 2018 Jan 9.
During the past decade, decreasing the attrition rate of drug development candidates reaching the market has become one of the major challenges in pharmaceutical research and drug development (R&D). To facilitate the decision-making process, and to increase the probability of rapidly finding and developing high-quality compounds, a variety of multiparametric guidelines, also known as rules and ligand efficiency (LE) metrics, have been developed. However, what are the 'best' descriptors and how far can we simplify these drug-likeness prediction tools in terms of the numerous, complex properties that they relate to?
在过去的十年中，降低进入市场的药物开发候选物的损耗率已成为药物研究和开发 (R&D) 中的主要挑战之一。为了促进决策过程，并提高快速发现和开发高质量化合物的概率，已经开发了各种多参数指南，也称为规则和配体效率 (LE) 指标。然而，“最佳”描述符是什么，以及我们可以在多大程度上简化这些药物相似性预测工具，考虑到它们所涉及的众多复杂性质？
