Ming Hao, Ph.D.

haomingcs@gmail.com

Publications

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Publication List

1. Hao M, Li Y, Wang Y, Zhang S. Prediction of PKCθ inhibitory activity using the random forest algorithm. International Journal of Molecular Sciences 11, 3413-3433 (2010).
2. Hao M, Li Y, Zhang S, Yang W Investigation on the binding mode of benzothiophene analogues as potent factor IXa (FIXa) inhibitors in thrombosis by CoMFA, docking and molecular dynamics studies. Journal of Enzyme Inhibition and Medicinal Chemistry 26, 792-804 (2011).
3. Hao M, Li Y, Wang Y, Zhang S. Prediction of P2Y12 antagonists using a novel genetic algorithm-support vector machine coupled approach. Analytica Chimica Acta 690, 53-63 (2011).
4. Hao M, Li Y, Wang Y, Zhang S. A classification study of respiratory syncytial virus (RSV) inhibitors by variable selection with random forest. International Journal of Molecular Sciences 12, 1259-1280 (2011).
5. Hao M, Li Y, Wang Y, Zhang S. A classification study of human β3-adrenergic receptor agonists using BCUT descriptors. Molecular Diversity 15, 877-887 (2011).
6. Hao M, Li Y, Wang Y, Yan Y, Zhang S, Li G, Yang L. Combined 3D-QSAR, molecular docking, and molecular dynamics study on piperazinyl-glutamate-pyridines/pyrimidines as potent P2Y12 antagonists for inhibition of platelet aggregation. Journal of Chemical Information and Modeling 51, 2560-2572 (2011).
7. Hao M, Li Y, Li H, Zhang S. Investigation of the structure requirement for 5-HT6 binding affinity of arylsulfonyl derivatives: a computational study. International Journal of Molecular Sciences 12, 5011-5030 (2011).
8. Zhang M, Zhang X, Li Y, Fan W, Wang Y, Hao M, Zhang S, Ai C. Investigation on quantitative structure activity relationships and pharmacophore modeling of a series of mGluR2 antagonists. International Journal of Molecular Sciences 12, 5999-6023 (2011).
9. Hao M, Zhang X, Ren H, Li Y, Zhang S, Luo F, Ji M, Li G, Yang L. In silico identification of structure requirement for novel thiazole and oxazole derivatives as potent fructose 1,6-bisphosphatase inhibitors. International Journal of Molecular Sciences 12, 8161-8180 (2011).
10. Mao Y, Li Y, Hao M, Zhang S, Ai C. Docking, molecular dynamics and quantitative structure-activity relationship studies for HEPTs and DABOs as HIV-1 reverse transcriptase inhibitors. Journal of Molecular Modeling 18, 2185-2198 (2012).
11. Li Y^#, Hao M^#, Ren H, Zhang S, Wang X, Ma M, Li G, Yang L. Exploring the structure requirement for PKCθ inhibitory activity of pyridinecarbonitrile derivatives: an in-silico analysis. Journal of Molecular Graphics and Modelling 34, 76-88 (2012). (^#co-first author)
12. Hao M, Zhang S, Qiu J. Toward the prediction of FBPase inhibitory activity using chemoinformatics methods. International Journal of Molecular Sciences 13, 7015-7037 (2012).
13. Hao M; Ren H, Luo F, Zhang S, Qiu J, Ji M, Si H, Li G. A computational study on thiourea analogs as potent MK-2 inhibitors. International Journal of Molecular Sciences 13, 7057-7079 (2012).
14. Hao M, Cheng T, Wang Y, Bryant S. Web search and data mining of natural products and their bioactivities in PubChem. Science China Chemistry 56, 1424-1435 (2013).
15. Hao M, Wang Y, Bryant S. An efficient algorithm coupled with synthetic minority oversampling technique to classify imbalanced PubChem BioAssay data. Analytica Chimica Acta 806, 117-127 (2014).
16. Cheng T, Pan Y, Hao M, Wang Y, Bryant S. PubChem applications in drug discovery: a bibliometric analysis. Drug Discovery Today 19, 1751-1756 (2014).
17. Hao M, Wang Y, Bryant S. Improved prediction of drug-target interactions using regularized least squares integrating with kernel fusion technique. Analytica Chimica Acta 909, 41-50 (2016).
18. Hao M, Bryant S, Wang Y. Cheminformatics analysis of the AR agonist and antagonist datasets in PubChem. Journal of Cheminformatics 8, 37 (2016).
19. Hao M, Bryant S, Wang Y. Predicting drug-target interactions by dual-network integrated logistic matrix factorization. Scientific Reports 7, 40376 (2017).
20. Takeda T, Hao M, Cheng T, Bryant S, Wang Y. Predicting drug-drug interactions through drug structural similarities and interaction networks incorporating pharmacokinetics and pharmacodynamics knowledge. Journal of Cheminformatics 9, 16 (2017).
21. Cheng T, Hao M, Takeda T, Bryant S, Wang Y. Large-scale prediction of drug-target interaction: a data-centric review. The AAPS Journal 19, 1264-1275 (2017).
22. Hao M, Bryant S, Wang Y. Open-source chemogenomic data-driven algorithms for predicting drug-target interactions. Briefings in Bioinformatics (2018).
23. Hao M, Bryant S, Wang Y. A new chemoinformatics approach with improved strategies for effective predictions of potential drugs. Journal of Cheminformatics 10, 50 (2018).

Invited Journal Reviewer

1. ACS Omega
2. Applied Computing and Informatics
3. Briefings in Bioinformatics
4. Bioinformatics
5. BMC Systems Biology
6. BioData Mining
7. ChemMedChem
8. Chemistry & Biology, Cell Press
9. Chemistry Central Journal
10. Computational Biology and Chemistry
11. Chemical Biology & Drug Design
12. DATABASE: The Journal of Biological Databases and Curation
13. Ecotoxicology and Environmental Safety
14. Health Informatics Journal
15. Iranian Journal of Science and Technology, Transactions A: Science
16. IEEE Journal of Biomedical and Health Informatics
17. Journal of Cheminformatics
18. Journal of Molecular Graphics and Modelling
19. Journal of Neurotrauma
20. Journal of Receptors and Signal Transduction
21. Letter in Drug Design and Discovery
22. Molecular Genetics and Genomics
23. PLOS ONE
24. Scientific Reports
25. Statistical Methods in Medical Research
26. The AAPS Journal