Li Guo-Bo, Ji Sen, Yang Ling-Ling, Zhang Rong-Jie, Chen Kai, Zhong Lei, Ma Shuang, Yang Sheng-Yong
State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy, West China Hospital, West China Medical School, Sichuan University, Sichuan 610041, China.
State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy, West China Hospital, West China Medical School, Sichuan University, Sichuan 610041, China; College of Chemical Engineering, Sichuan University, Sichuan 610041, China.
Eur J Med Chem. 2015 Mar 26;93:523-38. doi: 10.1016/j.ejmech.2015.02.019. Epub 2015 Feb 14.
Lead optimization is one of the key steps in drug discovery, and currently it is carried out mostly based on experiences of medicinal chemists, which often suffers from low efficiency. In silico methods are thought to be useful in improving the efficiency of lead optimization. Here we describe a new in silico automatic tool for structure-based lead optimization, termed LEADOPT. The structural modifications in LEADOPT mainly include two operations: fragment growing and fragment replacing, which are restricted to carry out in the active pocket of target protein with the core scaffold structure of ligand kept unchanged. The bioactivity of the newly generated molecules is estimated by ligand efficiency rather than a commonly used scoring function. Twelve important pharmacokinetic and toxic properties are evaluated using SCADMET, a program for the prediction of pharmacokinetic and toxic properties. LEADOPT was first evaluated using two retrospective cases, in which it showed a very good performance. LEADOPT was then applied to the structural optimizations of the VEGFR2 inhibitor, sorafenib, and the SYK inhibitor, R406. Though just several compounds were synthesized, we have obtained some compounds that are more potent than sorafenib and R406 in enzymatic and functional assays. All of these have validated, at least to some extent, the effectiveness of LEADOPT.
先导化合物优化是药物发现中的关键步骤之一,目前主要基于药物化学家的经验进行,效率往往较低。计算机辅助方法被认为有助于提高先导化合物优化的效率。在此,我们描述了一种用于基于结构的先导化合物优化的新型计算机辅助自动工具,称为LEADOPT。LEADOPT中的结构修饰主要包括两种操作:片段生长和片段替换,这些操作被限制在目标蛋白的活性口袋中进行,同时保持配体的核心支架结构不变。通过配体效率而非常用的评分函数来评估新生成分子的生物活性。使用用于预测药代动力学和毒性性质的程序SCADMET评估12种重要的药代动力学和毒性性质。首先使用两个回顾性案例对LEADOPT进行评估,结果显示其表现非常出色。然后将LEADOPT应用于VEGFR2抑制剂索拉非尼和SYK抑制剂R406的结构优化中。尽管只合成了几种化合物,但我们已经获得了一些在酶促和功能测定中比索拉非尼和R406更有效的化合物。所有这些至少在一定程度上验证了LEADOPT的有效性。