Yang, Z.; He, J.; Lu, A.; Hou, T.; Cao, D., The Application of Negative Design to Design More Desirable Virtual Screening Library. J MED CHEM 2020,.[PDF]
Yang, Z.; He, J.; Lu, A.; Hou, T.; Cao, D., Frequent hitters: nuisance artifacts in high-throughput screening. DRUG DISCOV TODAY 2020,.[PDF]
Shen, C.; Hu, Y.; Wang, Z.; Zhang, X.; Zhong, H.; Wang, G.; Yao, X.; Xu, L.; Cao, D.; Hou, T., Can machine learning consistently improve the scoring power of classical scoring functions? Insights into the role of machine learning in scoring functions. BRIEF BIOINFORM 2020,.[PDF]
Wang, Z.; Wang, X.; Kang, Y.; Zhong, H.; Shen, C.; Yao, X.; Cao, D.; Hou, T., Binding affinity and dissociation pathway predictions for a series of USP7 inhibitors with pyrimidinone scaffold by multiple computational methods. PHYS CHEM CHEM PHYS 2020,.[PDF]
Shen, C.; Ding, J.; Wang, Z.; Cao, D.; Ding, X.; Hou, T., From machine learning to deep learning: Advances in scoring functions for protein-ligand docking. Wiley Interdisciplinary Reviews: Computational Molecular Science 2020, 10, e1429.[PDF]
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 CHEMINFORMATICS 2020, 12, 1-26.[PDF]
2019
Yang, Z.; Yang, Z.; Dong, J.; Wang, L.; Zhang, L.; Ding, J.; Ding, X.; Lu, A.; Hou, T.; Cao, D., Structural Analysis and Identification of Colloidal Aggregators in Drug Discovery. J CHEM INF MODEL 2019, 59, 3714-3726.[PDF]
Wen, M.; Deng, Z.; Jiang, S.; Guan, Y.; Wu, H.; Wang, X.; Xiao, S.; Zhang, Y.; Yang, J.; Cao, D., Identification of a novel Bcl-2 inhibitor by ligand-based screening and investigation of its anti-Cancer effect on human breast Cancer cells. FRONT PHARMACOL 2019, 10, 391.[PDF]
Yun, Y.; Li, H.; Deng, B.; Cao, D., An overview of variable selection methods in multivariate analysis of near-infrared spectra. TrAC Trends in Analytical Chemistry 2019,.[PDF]
Ye, W.; Yang, S.; Zhang, L.; Deng, Z.; Li, W.; Zhang, J.; Zhang, L.; Yun, Y.; Chen, A. F.; Cao, D., Multistep virtual screening for rapid identification of G Protein-Coupled Receptors Kinase 2 inhibitors for heart failure treatment. CHEMOMETR INTELL LAB 2019, 185, 32-40.[PDF]
Jiang, S.; Chen, X.; Ge, P.; Wang, X.; Lao, Y.; Xiao, S.; Zhang, Y.; Yang, J.; Xu, X.; Cao, D., Tubeimoside-1, a triterpenoid saponin, induces cytoprotective autophagy in human breast cancer cells in vitro via Akt-mediated pathway. ACTA PHARMACOL SIN 2019, 40, 919-928.[PDF]
Fu, L.; Liu, L.; Yang, Z.; Pan, L.; Ding, J.; Yun, Y.; Lu, A.; Hou, T.; Cao, D., Systematic Modeling of logD7. 4 Based on Ensemble Machine Learning, Group Contribution and Matched Molecular Pair Analysis. J CHEM INF MODEL 2019,.[PDF]
Ye, W.; Zhang, L.; Guan, Y.; Xue, W.; Chen, A. F.; Cao, Q.; Cheng, Y.; Cao, D., Virtual screening and experimental validation of eEF2K inhibitors by combining homology modeling, QSAR and molecular docking from FDA approved drugs. NEW J CHEM 2019, 43, 19097-19106.[PDF]
Dong, J., Zhu, M. F., Yun, Y. H., Lu, A. P., Hou, T. J., & Cao, D. S., BioMedR: an R/CRAN package for integrated data analysis pipeline in biomedical study. Briefings in Bioinformatics. 2019,.[PDF]
Jiang, S.; Chen, X.; Ge, P.; Wang, X.; Lao, Y.; Xiao, S.; Zhang, Y.; Yang, J.; Xu, X.; Cao, D., Tubeimoside-1, a triterpenoid saponin, induces cytoprotective autophagy in human breast cancer cells in vitro via Akt-mediated pathway. ACTA PHARMACOL SIN 2019, 40, 919-928.[PDF]
2018
Liu, L.; Fu, L.; Zhang, J.; Wei, H.; Ye, W.; Deng, Z.; Zhang, L.; Cheng, Y.; Ouyang, D.; Cao, Q., Three-Level Hepatotoxicity Prediction System Based on Adverse Hepatic Effects. MOL PHARMACEUT 2018, 16, 393-408.[PDF]
Dong, J.; Wang, N.; Yao, Z.; Zhang, L.; Cheng, Y.; Ouyang, D.; Lu, A.; Cao, D., ADMETlab: a platform for systematic ADMET evaluation based on a comprehensively collected ADMET database. J CHEMINFORMATICS 2018, 10, 29.[PDF]
Dong, J.; Yao, Z.; Zhang, L.; Luo, F.; Lin, Q.; Lu, A.; Chen, A. F.; Cao, D., PyBioMed: a python library for various molecular representations of chemicals, proteins and DNAs and their interactions. J CHEMINFORMATICS 2018, 10, 16.[PDF]
Wang, N.; Dong, J.; Zhang, L.; Ouyang, D.; Cheng, Y.; Chen, A. F.; Lu, A.; Cao, D., HAMdb: a database of human autophagy modulators with specific pathway and disease information. J CHEMINFORMATICS 2018, 10, 1-8.[PDF]
2017
Yin-Hua Deng, Ning-Ning Wang, Zhen-Xing Zou, Lin Zhang, Kang-Ping Xu, Alex F. Chen, Dong-Sheng Cao and Gui-Shan Tan, Multi-Target Screening and Experimental Validation of Natural Products from Selaginella Plants against Alzheimer's Disease, Front. Pharmacol., 8:539.[PDF]
Jie Dong, Zhi-Jiang Yao, Min-Feng Zhu, Ning-Ning Wang, Ben Lu, Alex F Chen, Ai-Ping Lu, Hongyu Miao, Wen-Bin Zeng, Dong-Sheng Cao. ChemSAR: an online pipelining platform for molecular SAR modeling. Journal of Cheminformatics. 2017, 9(1): 27. [PDF]
Lin Zhang Tao Yang,Fang Zhu,Tianxiao Zhou,Dong-Sheng Cao,Qinlu Lin. Label-free, Water-soluble Fluorescent Peptide Probe for a Sensitive and Selective Determination of Copper Ions. Analytical Sciences. 2017, 33(2): 191-196. [PDF]
Gui-Shan Tan Zhen-Xing Zou,Kang-Ping Xu,Ping-Sheng Xu,Xia Yu,Dong-Sheng Cao. Seladoeflavones A-F, six novel flavonoids from Selaginella doederleinii. Fitoterapia. 2017, 116: 66-71. [PDF]
Gui-Shan Tan Zhen-Xing Zou,Ping-Sheng Xu,Guogang Zhang,Dong-Sheng Cao,Kang-Ping Xu. Selagintriflavonoids with BACE1 inhibitory activity from the fern Selaginella doederleinii. Phytochemistry. 2017, 134: 114-121. [PDF]
Ning-Ning Wang, Chen Huang, Jie Dong, Zhi-Jiang Yao, Min-Feng Zhu, Zhen-Ke Deng, Ben Lv, Ai-Ping Lu, Alex F Chen, Dong-Sheng Cao. Predicting human intestinal absorption with modified random forest approach: a comprehensive evaluation of molecular representation, unbalanced data, and applicability domain issues. RSC Advances. 2017, 7(31): 19007-19018. [PDF]
2016
Jie Dong, Zhi-Jiang Yao, Ming Wen, Min-Feng Zhu, Ning-Ning Wang, Hong-Yu Miao, Ai-Ping Lu, Wen-Bin Zeng and Dong-Sheng Cao.BioTriangle: a web-accessible platform for generating various molecular representations for chemicals, proteins, DNAs/RNAs and their interactions. Journal of Cheminformatics 2016, 8:34 [PDF]
NingNing Wang, Jie Dong, YinHua Deng, et al. ADME Properties Evaluation in Drug Discovery: Prediction of Caco-2 Cell Permeability Using a Combination of NSGA-II and Boosting. Journal of Chemical Information and Modeling. 2016, 56 (4), pp 763–773 [PDF]
Zhi-Jiang Yao , Jie Dong , Yu-Jing Che , Min-Feng Zhu, Ming Wen, Ning-Ning Wang, Shan Wang, Ai-Ping Lu, Dong-Sheng Cao. TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models. Journal of Computer-Aided Molecular Design, 2016 May 11. [PDF]
Yizeng Liang, Qing-Song Xu, Hong-Dong Li, Dong-Sheng Cao. Support vector machines and their application in chemistry and biotechnology. CRC Press, 2016.4.19. [PDF]
Qing-Song Xu, Jian Xu, Dong-Sheng Cao, Yi-Zeng Liang. Boosting in block variable subspaces: An approach of additive modeling for structure–activity relationship. Chemometrics and Intelligent Laboratory Systems. 2016, 152: 134-139.[PDF]
Yong-Huan Yun, Bai-Chuan Deng, Dong-Sheng Cao, Wei-Ting Wang, Yi-Zeng Liang. Variable importance analysis based on rank aggregation with applications in metabolomics for biomarker discovery. Analytica chimica acta. 2016, 911: 27-34.[PDF]
Bai-Chuan Deng, Yong-Huan Yun, Dong-Sheng Cao, Yu-Long Yin, Wei-Ting Wang, Hong-Mei Lu, Qian-Yi Luo, Yi-Zeng Liang. A bootstrapping soft shrinkage approach for variable selection in chemical modeling. Analytica chimica acta. 2016, 908: 63-74.[PDF]
Liang Shen, Dongsheng Cao, Qingsong Xu, Xin Huang, Nan Xiao, Yizeng Liang. A novel local manifold-ranking based K-NN for modeling the regression between bioactivity and molecular descriptors. Chemometrics and Intelligent Laboratory Systems. 2016, 151: 71-77.[PDF]
Jian-Hua Huang, Hua-Lin Xie, Jun Yan, Dong-Sheng Cao, Hong-Mei Lu, Qing-Song Xu, Yi-Zeng Liang. Interpretation of type 2 diabetes mellitus relevant GC-MS metabolomics fingerprints by using random forests (vol 5, pg 4883, 2013). ANALYTICAL METHODS. 2013, 5(18): 4883-4889.[PDF]
Ming Wen, Bai-Chuan Deng, Dong-Sheng Cao, Yong-Huan Yun, Rui-Han Yang, Hong-Mei Lu, Yi-Zeng Liang. The model adaptive space shrinkage (MASS) approach: a new method for simultaneous variable selection and outlier detection based on model population analysis. Analyst. 2016, 141(19): 5586-5597.[PDF]
2015
Jie Dong, Dong-Sheng Cao, Hong-Yu Miao, Shao Liu, Bai-Chuan Deng, Yong-Huan Yun, Ning-Ning Wang, Ai-Ping Lu, Wen-Bin Zeng, Alex Chen. ChemDes: an integrated web-based platform for molecular descriptor and fingerprint computation. Journal of Cheminformatics 2015, 7:60[PDF]
Cao, D‐S., N. Xiao, Y‐J. Li, W‐B. Zeng, Y‐Z. Liang, A‐P. Lu, Q‐S. Xu, and A. F. Chen. "Integrating Multiple Evidence Sources to Predict Adverse Drug Reactions Based on a Systems Pharmacology Model." CPT: pharmacometrics & systems pharmacology 4, no. 9 (2015): 498-506.[PDF]
Cao, Dong-Sheng, Jie Dong, Ning-Ning Wang, Ming Wen, Bai-Chuan Deng, Wen-Bin Zeng, Qing-Song Xu, Yi-Zeng Liang, Ai-Ping Lu, and Alex F. Chen. "In silico toxicity prediction of chemicals from EPA toxicity database by kernel fusion-based support vector machines." Chemometrics and Intelligent Laboratory Systems 146 (2015): 494-502.[PDF]
Huang, K., Liu, M., Liu, Z., Cao, D., Hou, J., & Zeng, W. (2015). Ratiometric and colorimetric detection of hydrogen sulfide with high selectivity and sensitivity using a novel FRET-based fluorescence probe. Dyes and Pigments, 118, 88-94.[PDF]
Wang, J. B., Cao, D. S., Zhu, M. F., Yun, Y. H., Xiao, N., & Liang, Y. Z. (2015). In silico evaluation of logD7. 4 and comparison with other prediction methods. Journal of Chemometrics.[HTML]
Xu, P., Huang, K., Cao, D., & Zeng, W. (2015). A Green and Efficient One-pot Synthesis of 1, 2, 3-Trisubstituted Pyrroles via Iodine-catalyzed Tandem Reaction. Letters in Organic Chemistry, 12(4), 290-298.[HTML]
Deng, B. C., Yun, Y. H., Liang, Y. Z., Cao, D. S., Xu, Q. S., Yi, L. Z., & Huang, X. (2015). A new strategy to prevent over-fitting in partial least squares models based on model population analysis. Analytica Chimica Acta.[PDF]
Huang, K., Liu, M., Wang, X., Cao, D., Gao, F., Zhou, K., ... & Zeng, W. (2015). Cascade reaction and FRET-based fluorescent probe for the colorimetric and ratiometric signaling of hydrogen sulfide. Tetrahedron Letters.[HTML]
Cao, D. S., Liu, S., Zeng, W. B., & Liang, Y. Z. (2015). Sparse canonical correlation analysis applied to‐omics studies for integrative analysis and biomarker discovery. Journal of Chemometrics.[PDF]
Cao, D., He, R., Zhang, M., Sun, Z., & Tan, T. (2015, March). Real-world gender recognition using multi-order LBP and localized multi-boost learning. In Identity, Security and Behavior Analysis (ISBA), 2015 IEEE International Conference on (pp. 1-6). IEEE.[HTML]
Lv, Y., Cao, D., Guo, F., Qian, Y., Wang, C., & Wang, D. (2015). Abdominal wall reconstruction using a combination of free tensor fasciae lata and anterolateral thigh myocutaneous flap: a prospective study in 16 patients.The American Journal of Surgery.[HTML]
Xiao, N., Cao, D. S., Zhu, M. F., & Xu, Q. S. (2015). protr/ProtrWeb: R package and web server for generating various numerical representation schemes of protein sequences. Bioinformatics, btv042.[HTML]
Wang, Y., Huang, J. J., Zhou, N., Cao, D. S., Dong, J., & Li, H. X. (2015). Incorporating PLS model information into particle swarm optimization for descriptor selection in QSAR/QSPR. Journal of Chemometrics.[PDF]
Xiao, Nan, Qing-Song Xu, and Dong-Sheng Cao. "protr: R package for generating various numerical representation schemes of protein sequence." (2014).[PDF]
Xiao, Nan, Dong-Sheng Cao, and Qing-Song Xu. "enpls: R Package for Ensemble Partial Least Squares Regression." (2014).[PDF]
Cao, D. S., Zhang, L. X., Tan, G. S., Xiang, Z., Zeng, W. B., Xu, Q. S., & Chen, A. F. (2014). Computational Prediction of Drug-Target Interactions Using Chemical, Biological, and Network Features. Molecular Informatics,33(10), 669-681.[PDF]
Cao, D. S., Xiao, N., Xu, Q. S., & Chen, A. F. (2014). Rcpi: R/Bioconductor package to generate various descriptors of proteins, compounds, and their interactions. Bioinformatics, btu624.[PDF]
Xiao, N., Cao, D., & Xu, Q. (2014). Rcpi: R/Bioconductor Package as an Integrated Informatics Platform in Drug Discovery.[PDF]
Xiao, N., Xu, Q. S., & Cao, D. S. (2014). protr: Protein Sequence-Derived Structural and Physiochemical Descriptors Calculation with R.[PDF]
Lou, Y., Peng, W. J., Cao, Y., Cao, D. S., Xie, J., & Li, H. H. (2014). The effectiveness of propranolol in treating infantile haemangiomas: a meta‐analysis including 35 studies. British journal of clinical pharmacology, 78(1), 44-57.[HTML]
Shi, S. H., Cai, Y. P., Cai, X. J., Zheng, X. Y., Cao, D. S., Ye, F. Q., & Xiang, Z. (2014). A network pharmacology approach to understanding the mechanisms of action of traditional medicine: bushenhuoxue formula for treatment of chronic kidney disease. PloS one, 9(3), e89123.[PDF]
Cao, D. S., Liu, S., Fan, L., & Liang, Y. Z. (2014). QSAR analysis of the effects of OATP1B1 transporter by structurally diverse natural products using a particle swarm optimization-combined multiple linear regression approach.Chemometrics and Intelligent Laboratory Systems, 130, 84-90.[PDF]
Yun, Y. H., Cao, D. S., Tan, M. L., Yan, J., Ren, D. B., Xu, Q. S., ... & Liang, Y. Z. (2014). A simple idea on applying large regression coefficient to improve the genetic algorithm-PLS for variable selection in multivariate calibration. Chemometrics and Intelligent Laboratory Systems, 130, 76-83.[PDF]
Yun, Y. H., Wang, W. T., Tan, M. L., Liang, Y. Z., Li, H. D., Cao, D. S., ... & Xu, Q. S. (2014). A strategy that iteratively retains informative variables for selecting optimal variable subset in multivariate calibration. Analytica chimica acta, 807, 36-43.[PDF]
Ying, X., Bing, Z., Bo-Tao, Z., Si-Yan, D., Li, Y., & Rou-Ke, L. (2014). Projected flood risks in China based on CMIP5. Advances in Climate Change Research, 5(2), 57-65.[HTML]
Huang, X., Xu, Q. S., Cao, D. S., Luo, Y. P., & Liang, Y. Z. (2014). Kernel k-nearest neighbor classifier based on decision tree ensemble for SAR modeling analysis. Analytical Methods, 6(17), 6621-6627.[HTML]
Xiao, N., Xu, Q., & Cao, D. (2014). protr: Protein Sequence Descriptor Calculation and Similarity Computation with R. R package version 0.2-1, URL http://CRAN. R-project. org/package= protr.[PDF]
2013
He, M., Cao, D. S., Liang, Y. Z., Li, Y. P., Liu, P. L., Xu, Q. S., & Huang, R. B. (2013). Pressor mechanism evaluation for phytochemical compounds using in silico compound–protein interaction prediction. Regulatory Toxicology and Pharmacology, 67(1), 115-124.[PDF]
Cao, D. S., Liang, Y. Z., Yan, J., Tan, G. S., Xu, Q. S., & Liu, S. (2013). PyDPI: freely available Python package for chemoinformatics, bioinformatics, and chemogenomics studies. Journal of chemical information and modeling,53(11), 3086-3096.[PDF]
Cao, D. S., Zhou, G. H., Liu, S., Zhang, L. X., Xu, Q. S., He, M., & Liang, Y. Z. (2013). Large-scale prediction of human kinase–inhibitor interactions using protein sequences and molecular topological structures. Analytica chimica acta, 792, 10-18.[PDF]
Yun, Y. H., Li, H. D., Wood, L. R., Fan, W., Wang, J. J., Cao, D. S., ... & Liang, Y. Z. (2013). An efficient method of wavelength interval selection based on random frog for multivariate spectral calibration. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 111, 31-36.[HTML]
Zhang, L., Tang, C., Cao, D., Zeng, Y., Tan, B., Zeng, M., ... & Liang, Y. (2013). Strategies for structure elucidation of small molecules using gas chromatography-mass spectrometric data. TrAC Trends in Analytical Chemistry, 47, 37-46.[PDF]
Huang, J. H., He, R. H., Yi, L. Z., Xie, H. L., Cao, D. S., & Liang, Y. Z. (2013). Exploring the relationship between 5′ AMP-activated protein kinase and markers related to type 2 diabetes mellitus. Talanta, 110, 1-7.[PDF]
Cao, D. S., Liang, Y. Z., Deng, Z., Hu, Q. N., He, M., Xu, Q. S., ... & Liu, S. (2013). Genome-scale screening of drug-target associations relevant to Ki using a chemogenomics approach. PloS one, 8(4), e57680.[PDF]
Cao, D. S., Xu, Q. S., Hu, Q. N., & Liang, Y. Z. (2013). ChemoPy: freely available python package for computational biology and chemoinformatics.Bioinformatics, btt105.[PDF]
Huang, X., Cao, D. S., Xu, Q. S., & Liang, Y. Z. (2013). A novel tree kernel partial least squares for modeling the structure–activity relationship. Journal of Chemometrics, 27(3-4), 43-49.[PDF]
Cao, D. S., Xu, Q. S., & Liang, Y. Z. (2013). propy: a tool to generate various modes of Chou’s PseAAC. Bioinformatics, 29(7), 960-962.[PDF]
He, M., Yan, J., Cao, D., Liu, S., Zhao, C., Liang, Y., ... & Zhang, Z. (2013). Identification of terpenoids from Ephedra combining with accurate mass and in-silico retention indices. Talanta, 103, 116-122.[PDF]
Huang, X., Cao, D. S., Xu, Q. S., Shen, L., Huang, J. H., & Liang, Y. Z. (2013). A novel tree kernel support vector machine classifier for modeling the relationship between bioactivity and molecular descriptors. Chemometrics and Intelligent Laboratory Systems, 120, 71-76.[HTML]
LUO, J., YU, F., LI, S. F., TANG, X. D., HU, Q., & CAO, D. (2013). Analysis of the Effects of 2011 Edition of Electronic Medical Record System and PASS Integration on Rational Drug Use Management in Our Hospital. China Pharmacy, 17, 005.[HTML]/li>
HE, R. H., LU, H. B., LI, Y. C., HUANG, J. G., YAN, J., CAO, D. S., ... & LIANG, Y. Z. (2013). Study on interpretation and reversion of tobaceo flavor based on bi-directional gradual simulation from chemical composition and fragrance characteristics. Chinese Journal of Analysis Laboratory, 3, 016.[HTML]
YANG, R., LU, H. B., LI, Y. C., HUANG, J. G., YAN, J., CAO, D. S., ... & LIANG, Y. Z. (2013). Exploration of Tobacco Flavor Analysis and Formulation Based on GC-MS and Chemometrics. Journal of Instrumental Analysis, 3, 009.[HTML]
Yun, Y. H., Li, H. D., Wood, L. R., Fan, W., Wang, J. J., Cao, D. S., ... & Liang, Y. Z. (2013). Spectr ochimica Acta Part A: Molecul ar and Biomo lecular Spectrosco py. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 111, 31-36.[PDF]
Yun, Y. H., Liang, Y. Z., Xie, G. X., Li, H. D., Cao, D. S., & Xu, Q. S. (2013). A perspective demonstration on the importance of variable selection in inverse calibration for complex analytical systems. Analyst, 138(21), 6412-6421.[PDF]
Huang, J. H., Xie, H. L., Yan, J., Cao, D. S., Lu, H. M., Xu, Q. S., & Liang, Y. Z. (2013). Interpretation of type 2 diabetes mellitus relevant GC-MS metabolomics fingerprints by using random forests. Analytical Methods,5(18), 4883-4889.[HTML]