Development of novel in silico methods for questing novel PgP inhibitors is crucial for the reversal of multi-drug resistance in cancer therapy. Here, we report machine learning based binary classification schemes to identify the PgP inhibitors from non-inhibitors using molecular solvation theory with excellent accuracy and precision. The excess chemical potential and partial molar volume in various solvents are calculated for PgP± (PgP inhibitors and non-inhibitors) compounds with the statistical-mechanical based three-dimensional reference interaction site model with the Kovalenko-Hirata closure approximation (3D-RISM-KH molecular theory of solvation). The statistical importance analysis of descriptors identified the 3D-RISM-KH based descriptors as top molecular descriptors for classification. Among the constructed classification models, the support vector machine predicted the test set of Pgp± compounds with highest accuracy and precision of ~ 97% for test set. The validation of models confirms the robustness of state-of-the-art molecular solvation theory based descriptors in identification of the Pgp± compounds.

中文翻译：

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使用机器学习分类模型和基于3D-RISM-KH理论的溶剂化能量描述符预测P-糖蛋白抑制剂。

寻求新型PgP抑制剂的新型计算机方法的开发对于逆转癌症治疗中的多药耐药性至关重要。在这里，我们报告基于机器学习的二元分类方案，使用分子溶剂化理论从非抑制剂中识别PgP抑制剂，具有极高的准确性和精密度。使用基于统计力学的三维参考相互作用位点模型，采用Kovalenko-Hirata闭合近似值（3D-RISM-），针对PgP±（PgP抑制剂和非抑制剂）化合物计算了各种溶剂中的过量化学势和部分摩尔体积。 KH溶剂化分子理论）。描述符的统计重要性分析确定了基于3D-RISM-KH的描述符作为分类的顶级分子描述符。在构建的分类模型中，支持向量机预测了Pgp±化合物的测试集，其准确度最高，测试集的准确度约为97％。模型的验证证实了基于最新分子溶剂化理论的描述子在识别Pgp±化合物方面的稳健性。