Classification of JAK1 Inhibitors and SAR Research by Machine Learning Methods
Janus kinase 1 (JAK1) is a key regulator of gene transcription, inhibition of JAK1 is an intervention for many diseases including rheumatoid arthritis and Crohn's disease. In this study, we collected a dataset containing 2982 JAK1 inhibitors, characterized molecules by MACCS fingerprints and Mo...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-12-01
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| Series: | Artificial Intelligence in the Life Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667318522000101 |
| _version_ | 1811297239550656512 |
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| author | Zhenwu Yang Yujia Tian Yue Kong Yushan Zhu Aixia Yan |
| author_facet | Zhenwu Yang Yujia Tian Yue Kong Yushan Zhu Aixia Yan |
| author_sort | Zhenwu Yang |
| collection | DOAJ |
| description | Janus kinase 1 (JAK1) is a key regulator of gene transcription, inhibition of JAK1 is an intervention for many diseases including rheumatoid arthritis and Crohn's disease. In this study, we collected a dataset containing 2982 JAK1 inhibitors, characterized molecules by MACCS fingerprints and Morgan fingerprints. We used support vector machine (SVM), decision tree (DT), random forest (RF) and extreme gradient boosting tree (XGBoost) algorithms to build 16 traditional machine learning classification models. Additionally, we utilized deep neural networks (DNN) to develop four deep learning models. The best model (Model 3B) built by RF and Morgan fingerprints achieved the accuracy (ACC) of 93.6% and Mathews correlation coefficient (MCC) of 0.87 on the test set. Furthermore, we made structure–activity relationship (SAR) analyses for JAK1 inhibitors, based on the output from the random forest models. After analyzing the important keys of two types of fingerprints, it was observed that some substructures such as pyrazole, pyrrolotriazolopyrimidine and pyrazolopyrimidine appeared frequently in highly active JAK1 inhibitors. |
| first_indexed | 2024-04-13T06:00:13Z |
| format | Article |
| id | doaj.art-2c3d889ce8be4c16921f17f1db9aec91 |
| institution | Directory Open Access Journal |
| issn | 2667-3185 |
| language | English |
| last_indexed | 2024-04-13T06:00:13Z |
| publishDate | 2022-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Artificial Intelligence in the Life Sciences |
| spelling | doaj.art-2c3d889ce8be4c16921f17f1db9aec912022-12-22T02:59:27ZengElsevierArtificial Intelligence in the Life Sciences2667-31852022-12-012100039Classification of JAK1 Inhibitors and SAR Research by Machine Learning MethodsZhenwu Yang0Yujia Tian1Yue Kong2Yushan Zhu3Aixia Yan4State Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, P.O. Box 53, Beijing University of Chemical Technology, 15 BeiSanHuan East Road, Beijing 100029, P. R. ChinaState Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, P.O. Box 53, Beijing University of Chemical Technology, 15 BeiSanHuan East Road, Beijing 100029, P. R. ChinaHyper-Dimension Insight Pharmaceuticals Ltd. Room 511, Block A, No. 2 C, DongSanHuan North Road, ChaoYang District, Beijing, P. R. ChinaNational Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, P. R ChinaState Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, P.O. Box 53, Beijing University of Chemical Technology, 15 BeiSanHuan East Road, Beijing 100029, P. R. China; Corresponding author.Janus kinase 1 (JAK1) is a key regulator of gene transcription, inhibition of JAK1 is an intervention for many diseases including rheumatoid arthritis and Crohn's disease. In this study, we collected a dataset containing 2982 JAK1 inhibitors, characterized molecules by MACCS fingerprints and Morgan fingerprints. We used support vector machine (SVM), decision tree (DT), random forest (RF) and extreme gradient boosting tree (XGBoost) algorithms to build 16 traditional machine learning classification models. Additionally, we utilized deep neural networks (DNN) to develop four deep learning models. The best model (Model 3B) built by RF and Morgan fingerprints achieved the accuracy (ACC) of 93.6% and Mathews correlation coefficient (MCC) of 0.87 on the test set. Furthermore, we made structure–activity relationship (SAR) analyses for JAK1 inhibitors, based on the output from the random forest models. After analyzing the important keys of two types of fingerprints, it was observed that some substructures such as pyrazole, pyrrolotriazolopyrimidine and pyrazolopyrimidine appeared frequently in highly active JAK1 inhibitors.http://www.sciencedirect.com/science/article/pii/S2667318522000101Deep neural networks (DNN)Janus kinase 1 (JAK1) inhibitorMolecular modelingStructure-activity relationshipSubstructure analysis |
| spellingShingle | Zhenwu Yang Yujia Tian Yue Kong Yushan Zhu Aixia Yan Classification of JAK1 Inhibitors and SAR Research by Machine Learning Methods Artificial Intelligence in the Life Sciences Deep neural networks (DNN) Janus kinase 1 (JAK1) inhibitor Molecular modeling Structure-activity relationship Substructure analysis |
| title | Classification of JAK1 Inhibitors and SAR Research by Machine Learning Methods |
| title_full | Classification of JAK1 Inhibitors and SAR Research by Machine Learning Methods |
| title_fullStr | Classification of JAK1 Inhibitors and SAR Research by Machine Learning Methods |
| title_full_unstemmed | Classification of JAK1 Inhibitors and SAR Research by Machine Learning Methods |
| title_short | Classification of JAK1 Inhibitors and SAR Research by Machine Learning Methods |
| title_sort | classification of jak1 inhibitors and sar research by machine learning methods |
| topic | Deep neural networks (DNN) Janus kinase 1 (JAK1) inhibitor Molecular modeling Structure-activity relationship Substructure analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2667318522000101 |
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