UNASSIGNED: Using known AR inhibitors and their chemical-physical descriptors we created the ML model for prediction of new AR inhibitors. The predicted inhibitors were tested by computational docking to the binding site of AR.
UNASSIGNED: Using cross-validation in order to find the most accurate ML model, we ended with final cross-validation accuracy of 90%. Computational docking testing of the predicted inhibitors gave a high level of correlation between the ML prediction score and binding free energy.
UNASSIGNED: Currently known AR inhibitors are not used yet for patients for several reasons. We think that new predicted AR inhibitors have the potential to possess more favorable characteristics to be successfully implemented after clinical testing. Exploring new inhibitors can improve patient well-being and lower surgical complications all while decreasing long-term medical expenses.
■使用已知的AR抑制剂及其化学物理描述符,我们创建了ML模型,用于预测新的AR抑制剂。通过与AR的结合位点的计算对接来测试预测的抑制剂。
■使用交叉验证以找到最准确的ML模型,我们最终的交叉验证准确率为90%.预测的抑制剂的计算对接测试给出了ML预测分数和结合自由能之间的高度相关性。
■目前已知的AR抑制剂由于几个原因尚未用于患者。我们认为,新的预测AR抑制剂有可能具有更有利的特性,以便在临床试验后成功实施。探索新的抑制剂可以改善患者的健康状况并降低手术并发症,同时降低长期医疗费用。