{"title":"利用深度学习预测g蛋白偶联受体:综述","authors":"Anuj Singh, A. Tiwari","doi":"10.5220/0010563200003161","DOIUrl":null,"url":null,"abstract":"The biggest super classes of the membrane proteins are G-protein coupled receptors as well as GPCRs are very significant for drug design goals. GPCRs are sometimes known as heptahelical receptor as well as seven-transmembrane receptor. GPCRs are accountable for several physicochemical and biological activities like cellular growth, neurotransmission, smell as well as vision. This paper presents a review related to current approaches to predict GPCRs. Extensive research on GPCRs have progressed to novel discoveries that open undiscovered and promising drug design opportunities and efficient drug-targeting Gprotein coupled receptors therapies. This paper concentrates primarily on the process of deep learning to","PeriodicalId":146672,"journal":{"name":"Proceedings of the 3rd International Conference on Advanced Computing and Software Engineering","volume":"83 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prediction of G-protein Coupled Receptors using Deep Learning: A Review\",\"authors\":\"Anuj Singh, A. Tiwari\",\"doi\":\"10.5220/0010563200003161\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The biggest super classes of the membrane proteins are G-protein coupled receptors as well as GPCRs are very significant for drug design goals. GPCRs are sometimes known as heptahelical receptor as well as seven-transmembrane receptor. GPCRs are accountable for several physicochemical and biological activities like cellular growth, neurotransmission, smell as well as vision. This paper presents a review related to current approaches to predict GPCRs. Extensive research on GPCRs have progressed to novel discoveries that open undiscovered and promising drug design opportunities and efficient drug-targeting Gprotein coupled receptors therapies. This paper concentrates primarily on the process of deep learning to\",\"PeriodicalId\":146672,\"journal\":{\"name\":\"Proceedings of the 3rd International Conference on Advanced Computing and Software Engineering\",\"volume\":\"83 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 3rd International Conference on Advanced Computing and Software Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5220/0010563200003161\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 3rd International Conference on Advanced Computing and Software Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5220/0010563200003161","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Prediction of G-protein Coupled Receptors using Deep Learning: A Review
The biggest super classes of the membrane proteins are G-protein coupled receptors as well as GPCRs are very significant for drug design goals. GPCRs are sometimes known as heptahelical receptor as well as seven-transmembrane receptor. GPCRs are accountable for several physicochemical and biological activities like cellular growth, neurotransmission, smell as well as vision. This paper presents a review related to current approaches to predict GPCRs. Extensive research on GPCRs have progressed to novel discoveries that open undiscovered and promising drug design opportunities and efficient drug-targeting Gprotein coupled receptors therapies. This paper concentrates primarily on the process of deep learning to