Mourad Stitou, H. Toufik, M. Bouachrine, H. Bih, F. Lamchouri
{"title":"用于定量构效关系研究的机器学习算法作为药物发现的新方法","authors":"Mourad Stitou, H. Toufik, M. Bouachrine, H. Bih, F. Lamchouri","doi":"10.1109/ISACS48493.2019.9068917","DOIUrl":null,"url":null,"abstract":"Developing machine learning algorithms have become important tools in drug discovery process. Nowadays, a variety of machine learning tools are used in quantitative structure-activity relationships (QSARs) to establish QSAR models. The 2D-QSAR analysis involves the study of quantitative relationships between the molecular descriptors and biological activity by using machine learning algorithms, such as partial least squares (PLS) and artificial neural networks (ANNs). The best linear 2D-QSAR model was developed through partial least squares (PLS) gave a high predictive ability (R2 = 0.87, F=52.80, R2pred = 0.80, Q2 = 0.77). Moreover, the non-linear artificial neural networks (ANNs) was shown better performance with Levenberge Marquardt (L-M) algorithm (architecture [3-3-1]: R2=0.94, R2pred=0.81, Q2=0.86). Those results uncovered that a_nO, PEOE_VSA+6 and Vsurf_R are important descriptors on which biological activity depends. Moreover, the retained 3D-QSAR model exhibits the best results (R2 = 0.94, R2pred = 0.80, Q2 = 0.67). However, the derived contour maps obtained by 3D-QSAR analysis indicate the favorable and unfavorable regions that could improve the cytotoxic activity. As a consequence, the established QSAR models based on machine learning methods could help us to understand the structural requirements necessary to design new compounds with improved biological activity.","PeriodicalId":312521,"journal":{"name":"2019 International Conference on Intelligent Systems and Advanced Computing Sciences (ISACS)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Machine learning algorithms used in Quantitative structure-activity relationships studies as new approaches in drug discovery\",\"authors\":\"Mourad Stitou, H. Toufik, M. Bouachrine, H. Bih, F. Lamchouri\",\"doi\":\"10.1109/ISACS48493.2019.9068917\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Developing machine learning algorithms have become important tools in drug discovery process. Nowadays, a variety of machine learning tools are used in quantitative structure-activity relationships (QSARs) to establish QSAR models. The 2D-QSAR analysis involves the study of quantitative relationships between the molecular descriptors and biological activity by using machine learning algorithms, such as partial least squares (PLS) and artificial neural networks (ANNs). The best linear 2D-QSAR model was developed through partial least squares (PLS) gave a high predictive ability (R2 = 0.87, F=52.80, R2pred = 0.80, Q2 = 0.77). Moreover, the non-linear artificial neural networks (ANNs) was shown better performance with Levenberge Marquardt (L-M) algorithm (architecture [3-3-1]: R2=0.94, R2pred=0.81, Q2=0.86). Those results uncovered that a_nO, PEOE_VSA+6 and Vsurf_R are important descriptors on which biological activity depends. Moreover, the retained 3D-QSAR model exhibits the best results (R2 = 0.94, R2pred = 0.80, Q2 = 0.67). However, the derived contour maps obtained by 3D-QSAR analysis indicate the favorable and unfavorable regions that could improve the cytotoxic activity. As a consequence, the established QSAR models based on machine learning methods could help us to understand the structural requirements necessary to design new compounds with improved biological activity.\",\"PeriodicalId\":312521,\"journal\":{\"name\":\"2019 International Conference on Intelligent Systems and Advanced Computing Sciences (ISACS)\",\"volume\":\"45 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 International Conference on Intelligent Systems and Advanced Computing Sciences (ISACS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISACS48493.2019.9068917\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Conference on Intelligent Systems and Advanced Computing Sciences (ISACS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISACS48493.2019.9068917","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Machine learning algorithms used in Quantitative structure-activity relationships studies as new approaches in drug discovery
Developing machine learning algorithms have become important tools in drug discovery process. Nowadays, a variety of machine learning tools are used in quantitative structure-activity relationships (QSARs) to establish QSAR models. The 2D-QSAR analysis involves the study of quantitative relationships between the molecular descriptors and biological activity by using machine learning algorithms, such as partial least squares (PLS) and artificial neural networks (ANNs). The best linear 2D-QSAR model was developed through partial least squares (PLS) gave a high predictive ability (R2 = 0.87, F=52.80, R2pred = 0.80, Q2 = 0.77). Moreover, the non-linear artificial neural networks (ANNs) was shown better performance with Levenberge Marquardt (L-M) algorithm (architecture [3-3-1]: R2=0.94, R2pred=0.81, Q2=0.86). Those results uncovered that a_nO, PEOE_VSA+6 and Vsurf_R are important descriptors on which biological activity depends. Moreover, the retained 3D-QSAR model exhibits the best results (R2 = 0.94, R2pred = 0.80, Q2 = 0.67). However, the derived contour maps obtained by 3D-QSAR analysis indicate the favorable and unfavorable regions that could improve the cytotoxic activity. As a consequence, the established QSAR models based on machine learning methods could help us to understand the structural requirements necessary to design new compounds with improved biological activity.