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Jiang D, Lei T, Wang Z, Shen C, Cao D, Hou T. ADMET evaluation in drug discovery. 20. Prediction of breast cancer resistance protein inhibition through machine learning. J Cheminform 2020; 12:16. [PMID: 33430990 PMCID: PMC7059329 DOI: 10.1186/s13321-020-00421-y] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Accepted: 02/20/2020] [Indexed: 12/14/2022] Open
Abstract
Breast cancer resistance protein (BCRP/ABCG2), an ATP-binding cassette (ABC) efflux transporter, plays a critical role in multi-drug resistance (MDR) to anti-cancer drugs and drug–drug interactions. The prediction of BCRP inhibition can facilitate evaluating potential drug resistance and drug–drug interactions in early stage of drug discovery. Here we reported a structurally diverse dataset consisting of 1098 BCRP inhibitors and 1701 non-inhibitors. Analysis of various physicochemical properties illustrates that BCRP inhibitors are more hydrophobic and aromatic than non-inhibitors. We then developed a series of quantitative structure–activity relationship (QSAR) models to discriminate between BCRP inhibitors and non-inhibitors. The optimal feature subset was determined by a wrapper feature selection method named rfSA (simulated annealing algorithm coupled with random forest), and the classification models were established by using seven machine learning approaches based on the optimal feature subset, including a deep learning method, two ensemble learning methods, and four classical machine learning methods. The statistical results demonstrated that three methods, including support vector machine (SVM), deep neural networks (DNN) and extreme gradient boosting (XGBoost), outperformed the others, and the SVM classifier yielded the best predictions (MCC = 0.812 and AUC = 0.958 for the test set). Then, a perturbation-based model-agnostic method was used to interpret our models and analyze the representative features for different models. The application domain analysis demonstrated the prediction reliability of our models. Moreover, the important structural fragments related to BCRP inhibition were identified by the information gain (IG) method along with the frequency analysis. In conclusion, we believe that the classification models developed in this study can be regarded as simple and accurate tools to distinguish BCRP inhibitors from non-inhibitors in drug design and discovery pipelines.![]()
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Affiliation(s)
- Dejun Jiang
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, People's Republic of China
| | - Tailong Lei
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, People's Republic of China
| | - Zhe Wang
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, People's Republic of China
| | - Chao Shen
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, People's Republic of China
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410004, Hunan, People's Republic of China.
| | - Tingjun Hou
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, People's Republic of China.
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Buttard F, Berthonneau C, Hiebel MA, Brière JF, Suzenet F. Organocatalytic aza-Michael Reaction to 3-Vinyl-1,2,4-triazines as a Valuable Bifunctional Platform. J Org Chem 2019; 84:3702-3714. [PMID: 30791682 DOI: 10.1021/acs.joc.9b00141] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
An unprecedented catalytic aza-Michael addition to substituted 3-vinyl-1,2,4-triazines, as original bifunctional platforms for the domino conjugate addition inverse-electron-demand hetero-Diels-Alder/retro-Diels-Alder ( ihDA/ rDA) reaction, was achieved using the highly acidic triflimide as an organocatalyst. Based on the use of alkoxyamine nucleophiles, this sequence not only highlights a rare example of the catalytic aza-Michael reaction to alkenylazaarenes but also proves to be useful for the elaboration of an array of biorelevant tetrahydro-[1,6]-naphthyridines.
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Affiliation(s)
- Floris Buttard
- Université d'Orléans, CNRS, ICOA, UMR 7311 , Orléans 45067 , France
| | | | | | | | - Franck Suzenet
- Université d'Orléans, CNRS, ICOA, UMR 7311 , Orléans 45067 , France
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Synthesis, characterization, and biological study of phenylalanine amide derivatives. MONATSHEFTE FUR CHEMIE 2016. [DOI: 10.1007/s00706-016-1700-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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Gros G, Fowler E, Hasserodt J. Coupling of an advanced tri-functional building block by reductive amination leads to a protected backbone of a new archetype of foldamer. Tetrahedron 2014. [DOI: 10.1016/j.tet.2014.07.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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