Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-Sen University, Guangzhou, 510006, China.
J Comput Aided Mol Des. 2018 Feb;32(2):347-361. doi: 10.1007/s10822-017-0092-8. Epub 2018 Jan 6.
PI3Kα is a promising drug target for cancer chemotherapy. In this paper, we report a strategy of combing ligand-based and structure-based virtual screening to identify new PI3Kα inhibitors. First, naïve Bayesian (NB) learning models and a 3D-QSAR pharmacophore model were built based upon known PI3Kα inhibitors. Then, the SPECS library was screened by the best NB model. This resulted in virtual hits, which were validated by matching the structures against the pharmacophore models. The pharmacophore matched hits were then docked into PI3Kα crystal structures to form ligand-receptor complexes, which are further validated by the Glide-XP program to result in structural validated hits. The structural validated hits were examined by PI3Kα inhibitory assay. With this screening protocol, ten PI3Kα inhibitors with new scaffolds were discovered with IC values ranging 0.44-31.25 μM. The binding affinities for the most active compounds 33 and 74 were estimated through molecular dynamics simulations and MM-PBSA analyses.
PI3Kα 是癌症化疗有前途的药物靶点。在本文中，我们报告了一种结合基于配体和基于结构的虚拟筛选策略，以识别新的 PI3Kα 抑制剂。首先，基于已知的 PI3Kα 抑制剂构建了朴素贝叶斯（NB）学习模型和 3D-QSAR 药效团模型。然后，使用最佳的 NB 模型对 SPECS 库进行筛选。这产生了虚拟命中物，然后通过将结构与药效团模型匹配来验证。将药效团匹配的命中物对接进 PI3Kα 晶体结构中，形成配体-受体复合物，然后使用 Glide-XP 程序进一步验证，得到结构验证的命中物。通过 PI3Kα 抑制测定法检查结构验证的命中物。通过这种筛选方案，发现了十个具有新骨架的 PI3Kα 抑制剂，其 IC 值范围为 0.44-31.25 μM。通过分子动力学模拟和 MM-PBSA 分析估算了最活性化合物 33 和 74 的结合亲和力。
