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 IC50 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.

中文翻译：

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通过结合基于配体和基于结构的虚拟筛选策略发现新的PI3Kα抑制剂。

PI3Kα是用于癌症化疗的有希望的药物靶标。在本文中，我们报告了结合基于配体和基于结构的虚拟筛选以识别新的PI3Kα抑制剂的策略。首先，基于已知的PI3Kα抑制剂建立了朴素的贝叶斯（NB）学习模型和3D-QSAR药效团模型。然后，通过最佳的NB模型筛选SPECS库。这产生了虚拟命中，通过将结构与药效团模型匹配来验证。然后，将药效基团匹配的匹配物停靠到PI3Kα晶体结构中，形成配体-受体复合物，然后通过Glide-XP程序对其进行进一步验证，以得到结构验证的匹配物。通过PI3Kα抑制分析检查结构验证的命中。有了这个筛选方案，发现了十种具有新支架的PI3Kα抑制剂，IC50值为0.44-31.25μM。通过分子动力学模拟和MM-PBSA分析估计了活性最高的化合物33和74的结合亲和力。