PDF(Pubmed)
Abstract：
UNASSIGNED: Patients with diabetes mellitus have an elevated chance of developing cataracts, a degenerative vision-impairing condition often needing surgery. The process of the reduction of glucose to sorbitol in the lens of the human eye that causes cataracts is managed by the Aldose Reductase Enzyme (AR), and it is been found that AR inhibitors may mitigate the onset of diabetic cataracts. There exists a large pool of natural and synthetic AR inhibitors that can prevent diabetic complications, and the development of a machine-learning (ML) prediction model may bring new AR inhibitors with better characteristics into clinical use.
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.
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.
摘要：
■糖尿病患者患白内障的机会增加，一种退化性视力受损的状况，经常需要手术。导致白内障的人眼晶状体中葡萄糖还原为山梨糖醇的过程由醛糖还原酶（AR）管理，并且发现AR抑制剂可以减轻糖尿病性白内障的发作。存在大量的天然和合成AR抑制剂，可以预防糖尿病并发症，而机器学习（ML）预测模型的开发可能会将具有更好特性的新型AR抑制剂带入临床。
■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。
■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。
