Department of Mechanical Engineering, 10-203 Donadeo Innovation Centre for Engineering, University of Alberta, 9211-116 Street NW, Edmonton, AB, T6G 1H9, Canada.
Nanotechnology Research Centre, 11421 Saskatchewan Drive, Edmonton, AB, T6G 2M9, Canada.
J Comput Aided Mol Des. 2019 Nov;33(11):965-971. doi: 10.1007/s10822-019-00253-5. Epub 2019 Nov 19.
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.
开发新型的计算机方法来寻找新型的 PgP 抑制剂对于逆转癌症治疗中的多药耐药性至关重要。在这里，我们报告了基于机器学习的二元分类方案，该方案使用分子溶剂化理论，通过出色的准确性和精度来识别 PgP 抑制剂和非抑制剂。利用基于统计力学的三维参考相互作用位点模型（Kovalenko-Hirata 封闭近似）（3D-RISM-KH 溶剂化分子理论）计算了 PgP±（PgP 抑制剂和非抑制剂）化合物在各种溶剂中的过剩化学势和偏摩尔体积。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。在所构建的分类模型中，支持向量机对 Pgp±化合物的测试集进行预测，测试集的准确率和精度最高约为 97%。模型的验证证实了基于最先进的分子溶剂化理论的描述符在识别 Pgp±化合物方面的稳健性。
