910 related articles for article (PubMed ID: 28595388)
1. ADMET Evaluation in Drug Discovery. Part 17: Development of Quantitative and Qualitative Prediction Models for Chemical-Induced Respiratory Toxicity.
Lei T; Chen F; Liu H; Sun H; Kang Y; Li D; Li Y; Hou T
Mol Pharm; 2017 Jul; 14(7):2407-2421. PubMed ID: 28595388
[TBL] [Abstract][Full Text] [Related]
2. ADMET Evaluation in Drug Discovery. 18. Reliable Prediction of Chemical-Induced Urinary Tract Toxicity by Boosting Machine Learning Approaches.
Lei T; Sun H; Kang Y; Zhu F; Liu H; Zhou W; Wang Z; Li D; Li Y; Hou T
Mol Pharm; 2017 Nov; 14(11):3935-3953. PubMed ID: 29037046
[TBL] [Abstract][Full Text] [Related]
3. ADMET Evaluation in Drug Discovery. 16. Predicting hERG Blockers by Combining Multiple Pharmacophores and Machine Learning Approaches.
Wang S; Sun H; Liu H; Li D; Li Y; Hou T
Mol Pharm; 2016 Aug; 13(8):2855-66. PubMed ID: 27379394
[TBL] [Abstract][Full Text] [Related]
4. An Explainable Supervised Machine Learning Model for Predicting Respiratory Toxicity of Chemicals Using Optimal Molecular Descriptors.
Jaganathan K; Tayara H; Chong KT
Pharmaceutics; 2022 Apr; 14(4):. PubMed ID: 35456666
[TBL] [Abstract][Full Text] [Related]
5. ADMET evaluation in drug discovery: 15. Accurate prediction of rat oral acute toxicity using relevance vector machine and consensus modeling.
Lei T; Li Y; Song Y; Li D; Sun H; Hou T
J Cheminform; 2016; 8():6. PubMed ID: 26839598
[TBL] [Abstract][Full Text] [Related]
6. Do we need different machine learning algorithms for QSAR modeling? A comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets.
Wu Z; Zhu M; Kang Y; Leung EL; Lei T; Shen C; Jiang D; Wang Z; Cao D; Hou T
Brief Bioinform; 2021 Jul; 22(4):. PubMed ID: 33313673
[TBL] [Abstract][Full Text] [Related]
7. ADMET evaluation in drug discovery. 20. Prediction of breast cancer resistance protein inhibition through machine learning.
Jiang D; Lei T; Wang Z; Shen C; Cao D; Hou T
J Cheminform; 2020 Mar; 12(1):16. PubMed ID: 33430990
[TBL] [Abstract][Full Text] [Related]
8. Targeting HIV/HCV Coinfection Using a Machine Learning-Based Multiple Quantitative Structure-Activity Relationships (Multiple QSAR) Method.
Wei Y; Li W; Du T; Hong Z; Lin J
Int J Mol Sci; 2019 Jul; 20(14):. PubMed ID: 31336592
[TBL] [Abstract][Full Text] [Related]
9. In silico prediction of drug-induced developmental toxicity by using machine learning approaches.
Zhang H; Mao J; Qi HZ; Ding L
Mol Divers; 2020 Nov; 24(4):1281-1290. PubMed ID: 31486961
[TBL] [Abstract][Full Text] [Related]
10. Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization.
Nishio M; Nishizawa M; Sugiyama O; Kojima R; Yakami M; Kuroda T; Togashi K
PLoS One; 2018; 13(4):e0195875. PubMed ID: 29672639
[TBL] [Abstract][Full Text] [Related]
11. Bioactive Molecule Prediction Using Extreme Gradient Boosting.
Babajide Mustapha I; Saeed F
Molecules; 2016 Jul; 21(8):. PubMed ID: 27483216
[TBL] [Abstract][Full Text] [Related]
12. Prediction of acute toxicity of emerging contaminants on the water flea Daphnia magna by Ant Colony Optimization-Support Vector Machine QSTR models.
Aalizadeh R; von der Ohe PC; Thomaidis NS
Environ Sci Process Impacts; 2017 Mar; 19(3):438-448. PubMed ID: 28234392
[TBL] [Abstract][Full Text] [Related]
13. ADMET Evaluation in Drug Discovery. 19. Reliable Prediction of Human Cytochrome P450 Inhibition Using Artificial Intelligence Approaches.
Wu Z; Lei T; Shen C; Wang Z; Cao D; Hou T
J Chem Inf Model; 2019 Nov; 59(11):4587-4601. PubMed ID: 31644282
[TBL] [Abstract][Full Text] [Related]
14. Predicting the reproductive toxicity of chemicals using ensemble learning methods and molecular fingerprints.
Feng H; Zhang L; Li S; Liu L; Yang T; Yang P; Zhao J; Arkin IT; Liu H
Toxicol Lett; 2021 Apr; 340():4-14. PubMed ID: 33421549
[TBL] [Abstract][Full Text] [Related]
15. Computational models for the classification of mPGES-1 inhibitors with fingerprint descriptors.
Xia Z; Yan A
Mol Divers; 2017 Aug; 21(3):661-675. PubMed ID: 28484935
[TBL] [Abstract][Full Text] [Related]
16. Relevance Vector Machines: Sparse Classification Methods for QSAR.
Burden FR; Winkler DA
J Chem Inf Model; 2015 Aug; 55(8):1529-34. PubMed ID: 26158341
[TBL] [Abstract][Full Text] [Related]
17. QSAR modelling study of the bioconcentration factor and toxicity of organic compounds to aquatic organisms using machine learning and ensemble methods.
Ai H; Wu X; Zhang L; Qi M; Zhao Y; Zhao Q; Zhao J; Liu H
Ecotoxicol Environ Saf; 2019 Sep; 179():71-78. PubMed ID: 31026752
[TBL] [Abstract][Full Text] [Related]
18. Development and rigorous validation of antimalarial predictive models using machine learning approaches.
Danishuddin ; Madhukar G; Malik MZ; Subbarao N
SAR QSAR Environ Res; 2019 Aug; 30(8):543-560. PubMed ID: 31328578
[TBL] [Abstract][Full Text] [Related]
19. Application of a developed triple-classification machine learning model for carcinogenic prediction of hazardous organic chemicals to the US, EU, and WHO based on Chinese database.
Hao N; Sun P; Zhao W; Li X
Ecotoxicol Environ Saf; 2023 Apr; 255():114806. PubMed ID: 36948010
[TBL] [Abstract][Full Text] [Related]
20. In silico prediction of chemical acute oral toxicity using multi-classification methods.
Li X; Chen L; Cheng F; Wu Z; Bian H; Xu C; Li W; Liu G; Shen X; Tang Y
J Chem Inf Model; 2014 Apr; 54(4):1061-9. PubMed ID: 24735213
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
