Laboratoire d'Innovation Thérapeutique, UMR 7200 CNRS-Université de Strasbourg, 74 route du Rhin, 67400 Illkirch, France.
Pharmacol Ther. 2017 Jul;175:47-66. doi: 10.1016/j.pharmthera.2017.02.034. Epub 2017 Feb 14.
Drug discovery is a multidisciplinary and multivariate optimization endeavor. As such, in silico screening tools have gained considerable importance to archive, analyze and exploit the vast and ever-increasing amount of experimental data generated throughout the process. The current review will focus on the computer-aided prediction of the numerous properties that need to be controlled during the discovery of a preliminary hit and its promotion to a viable clinical candidate. It does not pretend to the almost impossible task of an exhaustive report but will highlight a few key points that need to be collectively addressed both by chemists and biologists to fuel the drug discovery pipeline with innovative and safe drug candidates.
药物发现是一项多学科、多变量的优化工作。因此，计算筛选工具在存储、分析和利用整个过程中产生的大量且不断增加的实验数据方面具有相当重要的意义。本综述将重点介绍计算机辅助预测在初步命中物的发现及其向可行的临床候选药物推进过程中需要控制的众多性质。它并不试图完成几乎不可能的任务，即进行详尽的报告，而是强调一些关键要点，这些要点需要化学家与生物学家共同解决，以便为药物发现管道提供创新且安全的候选药物。
