{"title":"将遗传算法与深度学习整合用于新型酪氨酸激酶抑制剂的生成和生物活性预测","authors":"Ricardo Romero","doi":"arxiv-2408.07155","DOIUrl":null,"url":null,"abstract":"The intersection of artificial intelligence and bioinformatics has enabled\nsignificant advancements in drug discovery, particularly through the\napplication of machine learning models. In this study, we present a combined\napproach using genetic algorithms and deep learning models to address two\ncritical aspects of drug discovery: the generation of novel tyrosine kinase\ninhibitors and the prediction of their bioactivity. The generative model\nleverages genetic algorithms to create new small molecules with optimized ADMET\n(absorption, distribution, metabolism, excretion, and toxicity) and\ndrug-likeness properties. Concurrently, a deep learning model is employed to\npredict the bioactivity of these generated molecules against tyrosine kinases,\na key enzyme family involved in various cellular processes and cancer\nprogression. By integrating these advanced computational methods, we\ndemonstrate a powerful framework for accelerating the generation and\nidentification of potential tyrosine kinase inhibitors, contributing to more\nefficient and effective early-stage drug discovery processes.","PeriodicalId":501022,"journal":{"name":"arXiv - QuanBio - Biomolecules","volume":"36 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Integration of Genetic Algorithms and Deep Learning for the Generation and Bioactivity Prediction of Novel Tyrosine Kinase Inhibitors\",\"authors\":\"Ricardo Romero\",\"doi\":\"arxiv-2408.07155\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The intersection of artificial intelligence and bioinformatics has enabled\\nsignificant advancements in drug discovery, particularly through the\\napplication of machine learning models. In this study, we present a combined\\napproach using genetic algorithms and deep learning models to address two\\ncritical aspects of drug discovery: the generation of novel tyrosine kinase\\ninhibitors and the prediction of their bioactivity. The generative model\\nleverages genetic algorithms to create new small molecules with optimized ADMET\\n(absorption, distribution, metabolism, excretion, and toxicity) and\\ndrug-likeness properties. Concurrently, a deep learning model is employed to\\npredict the bioactivity of these generated molecules against tyrosine kinases,\\na key enzyme family involved in various cellular processes and cancer\\nprogression. By integrating these advanced computational methods, we\\ndemonstrate a powerful framework for accelerating the generation and\\nidentification of potential tyrosine kinase inhibitors, contributing to more\\nefficient and effective early-stage drug discovery processes.\",\"PeriodicalId\":501022,\"journal\":{\"name\":\"arXiv - QuanBio - Biomolecules\",\"volume\":\"36 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - QuanBio - Biomolecules\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2408.07155\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuanBio - Biomolecules","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2408.07155","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Integration of Genetic Algorithms and Deep Learning for the Generation and Bioactivity Prediction of Novel Tyrosine Kinase Inhibitors
The intersection of artificial intelligence and bioinformatics has enabled
significant advancements in drug discovery, particularly through the
application of machine learning models. In this study, we present a combined
approach using genetic algorithms and deep learning models to address two
critical aspects of drug discovery: the generation of novel tyrosine kinase
inhibitors and the prediction of their bioactivity. The generative model
leverages genetic algorithms to create new small molecules with optimized ADMET
(absorption, distribution, metabolism, excretion, and toxicity) and
drug-likeness properties. Concurrently, a deep learning model is employed to
predict the bioactivity of these generated molecules against tyrosine kinases,
a key enzyme family involved in various cellular processes and cancer
progression. By integrating these advanced computational methods, we
demonstrate a powerful framework for accelerating the generation and
identification of potential tyrosine kinase inhibitors, contributing to more
efficient and effective early-stage drug discovery processes.