{"title":"结合双重机器学习过滤和片段置换优化方法、分子对接和动态模拟方法,开发新型 ALOX15 抑制剂。","authors":"Yinglin Liao, Peng Cao, Lianxiang Luo","doi":"10.1080/14756366.2024.2301756","DOIUrl":null,"url":null,"abstract":"<p><p>The oxidation of unsaturated lipids, facilitated by the enzyme Arachidonic acid 15-lipoxygenase (ALOX15), is an essential element in the development of ferroptosis. This study combined a dual-score exclusion strategy with high-throughput virtual screening, naive Bayesian and recursive partitioning machine learning models, the already established ALOX15 inhibitor i472, and a docking-based fragment substitution optimisation approach to identify potential ALOX15 inhibitors, ultimately leading to the discovery of three FDA-approved drugs that demonstrate optimal inhibitory potential against ALOX15. Through fragment substitution-based optimisation, seven new inhibitor structures have been developed. To evaluate their practicality, ADMET predictions and molecular dynamics simulations were performed. In conclusion, the compounds found in this study provide a novel approach to combat conditions related to ferroptosis-related injury by inhibiting ALOX15.</p>","PeriodicalId":15769,"journal":{"name":"Journal of Enzyme Inhibition and Medicinal Chemistry","volume":"39 1","pages":"2301756"},"PeriodicalIF":5.6000,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10791093/pdf/","citationCount":"0","resultStr":"{\"title\":\"Development of novel ALOX15 inhibitors combining dual machine learning filtering and fragment substitution optimisation approaches, molecular docking and dynamic simulation methods.\",\"authors\":\"Yinglin Liao, Peng Cao, Lianxiang Luo\",\"doi\":\"10.1080/14756366.2024.2301756\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The oxidation of unsaturated lipids, facilitated by the enzyme Arachidonic acid 15-lipoxygenase (ALOX15), is an essential element in the development of ferroptosis. This study combined a dual-score exclusion strategy with high-throughput virtual screening, naive Bayesian and recursive partitioning machine learning models, the already established ALOX15 inhibitor i472, and a docking-based fragment substitution optimisation approach to identify potential ALOX15 inhibitors, ultimately leading to the discovery of three FDA-approved drugs that demonstrate optimal inhibitory potential against ALOX15. Through fragment substitution-based optimisation, seven new inhibitor structures have been developed. To evaluate their practicality, ADMET predictions and molecular dynamics simulations were performed. In conclusion, the compounds found in this study provide a novel approach to combat conditions related to ferroptosis-related injury by inhibiting ALOX15.</p>\",\"PeriodicalId\":15769,\"journal\":{\"name\":\"Journal of Enzyme Inhibition and Medicinal Chemistry\",\"volume\":\"39 1\",\"pages\":\"2301756\"},\"PeriodicalIF\":5.6000,\"publicationDate\":\"2024-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10791093/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Enzyme Inhibition and Medicinal Chemistry\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1080/14756366.2024.2301756\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/12 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"BIOCHEMISTRY & MOLECULAR BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Enzyme Inhibition and Medicinal Chemistry","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1080/14756366.2024.2301756","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/12 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
Development of novel ALOX15 inhibitors combining dual machine learning filtering and fragment substitution optimisation approaches, molecular docking and dynamic simulation methods.
The oxidation of unsaturated lipids, facilitated by the enzyme Arachidonic acid 15-lipoxygenase (ALOX15), is an essential element in the development of ferroptosis. This study combined a dual-score exclusion strategy with high-throughput virtual screening, naive Bayesian and recursive partitioning machine learning models, the already established ALOX15 inhibitor i472, and a docking-based fragment substitution optimisation approach to identify potential ALOX15 inhibitors, ultimately leading to the discovery of three FDA-approved drugs that demonstrate optimal inhibitory potential against ALOX15. Through fragment substitution-based optimisation, seven new inhibitor structures have been developed. To evaluate their practicality, ADMET predictions and molecular dynamics simulations were performed. In conclusion, the compounds found in this study provide a novel approach to combat conditions related to ferroptosis-related injury by inhibiting ALOX15.
期刊介绍:
Journal of Enzyme Inhibition and Medicinal Chemistry publishes open access research on enzyme inhibitors, inhibitory processes, and agonist/antagonist receptor interactions in the development of medicinal and anti-cancer agents.
Journal of Enzyme Inhibition and Medicinal Chemistry aims to provide an international and interdisciplinary platform for the latest findings in enzyme inhibition research.
The journal’s focus includes current developments in:
Enzymology;
Cell biology;
Chemical biology;
Microbiology;
Physiology;
Pharmacology leading to drug design;
Molecular recognition processes;
Distribution and metabolism of biologically active compounds.