Search of inhibitors of aldose reductase for treatment of diabetic cataracts using machine learning
Search of inhibitors of aldose reductase for treatment of diabetic cataracts using machine learning
- 眼科实践与研究新进展 2023年3卷第4期 页码：187-191
- 作者机构：
1. MAP Program, University of California, San Diego, La Jolla,CA,USA
2. San Diego Supercomputer Center, University of California, San Diego, La Jolla,CA,USA
3. CureScience Institute,CA,San Diego,USA
4. Department of Neurosciences, University of California, San Diego, La Jolla,CA,USA
- 作者简介：
- 基金信息：
DOI：10.1016/j.aopr.2023.09.002
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Trevor Chen, Richard Chen, Alvin You, 等. Search of inhibitors of aldose reductase for treatment of diabetic cataracts using machine learning[J]. 眼科实践与研究新进展, 2023,3(4):187-191.
Search of inhibitors of aldose reductase for treatment of diabetic cataracts using machine learning[J]. AOPR, 2023,3(4):187-191.
Trevor Chen, Richard Chen, Alvin You, 等. Search of inhibitors of aldose reductase for treatment of diabetic cataracts using machine learning[J]. 眼科实践与研究新进展, 2023,3(4):187-191. DOI： 10.1016/j.aopr.2023.09.002.
Search of inhibitors of aldose reductase for treatment of diabetic cataracts using machine learning[J]. AOPR, 2023,3(4):187-191. DOI： 10.1016/j.aopr.2023.09.002.
摘要
Purpose,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.,Methods,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.,Results,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.,Conclusions,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.
Abstract
Purpose,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.,Methods,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.,Results,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.,Conclusions,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.
关键词
Diabetic cataractsAldose reductase inhibitorMachine learningDeep learning
Keywords
Diabetic cataractsAldose reductase inhibitorMachine learningDeep learning
references
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