|Therapeutic target database 2020: enriched resource for facilitating research and early development of targeted therapeutics|
Y Wang, S Zhang, F Li, Y Zhou, Y Zhang, Z Wang, R Zhang, J Zhu, Y Ren, ...
Nucleic acids research 48 (D1), D1031-D1041, 2020
|Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics|
YH Li, CY Yu, XX Li, P Zhang, J Tang, Q Yang, T Fu, X Zhang, X Cui, G Tu, ...
Nucleic acids research 46 (D1), D1121-D1127, 2018
|NOREVA: normalization and evaluation of MS-based metabolomics data|
B Li, J Tang, Q Yang, S Li, X Cui, Y Li, Y Chen, W Xue, X Li, F Zhu
Nucleic acids research 45 (W1), W162-W170, 2017
|Therapeutic target database update 2016: enriched resource for bench to clinical drug target and targeted pathway information|
H Yang, C Qin, YH Li, L Tao, J Zhou, CY Yu, F Xu, Z Chen, F Zhu, ...
Nucleic acids research 44 (D1), D1069-D1074, 2016
|Consistent gene signature of schizophrenia identified by a novel feature selection strategy from comprehensive sets of transcriptomic data|
Q Yang, B Li, J Tang, X Cui, Y Wang, X Li, J Hu, Y Chen, W Xue, Y Lou, ...
Briefings in Bioinformatics 21 (3), 1058-1068, 2020
|ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies|
J Tang, J Fu, Y Wang, B Li, Y Li, Q Yang, X Cui, J Hong, X Li, Y Chen, ...
Briefings in bioinformatics 21 (2), 621-636, 2020
|Therapeutic target database update 2014: a resource for targeted therapeutics|
C Qin, C Zhang, F Zhu, F Xu, SY Chen, P Zhang, YH Li, SY Yang, YQ Wei, ...
Nucleic acids research 42 (D1), D1118-D1123, 2014
|Clinical trials, progression-speed differentiating features and swiftness rule of the innovative targets of first-in-class drugs|
YH Li, XX Li, JJ Hong, YX Wang, JB Fu, H Yang, CY Yu, FC Li, J Hu, ...
Briefings in Bioinformatics 21 (2), 649-662, 2020
|Computational identification of the binding mechanism of a triple reuptake inhibitor amitifadine for the treatment of major depressive disorder|
W Xue, P Wang, G Tu, F Yang, G Zheng, X Li, X Li, Y Chen, X Yao, F Zhu
Physical Chemistry Chemical Physics 20 (9), 6606-6616, 2018
|SVM-Prot 2016: a web-server for machine learning prediction of protein functional families from sequence irrespective of similarity|
YH Li, JY Xu, L Tao, XF Li, S Li, X Zeng, SY Chen, P Zhang, C Qin, ...
PloS one 11 (8), e0155290, 2016
|Exploring the binding mechanism of metabotropic glutamate receptor 5 negative allosteric modulators in clinical trials by molecular dynamics simulations|
T Fu, G Zheng, G Tu, F Yang, Y Chen, X Yao, X Li, W Xue, F Zhu
ACS chemical neuroscience 9 (6), 1492-1502, 2018
|Clinical success of drug targets prospectively predicted by in silico study|
F Zhu, XX Li, SY Yang, YZ Chen
Trends in Pharmacological Sciences 39 (3), 229-231, 2018
|MMEASE: online meta-analysis of metabolomic data by enhanced metabolite annotation, marker selection and enrichment analysis|
Q Yang, B Li, S Chen, J Tang, Y Li, Y Li, S Zhang, C Shi, Y Zhang, M Mou, ...
Journal of Proteomics 232, 104023, 2021
|Discovery of the consistently well-performed analysis chain for SWATH-MS based pharmacoproteomic quantification|
J Fu, J Tang, Y Wang, X Cui, Q Yang, J Hong, X Li, S Li, Y Chen, W Xue, ...
Frontiers in pharmacology 9, 681, 2018
|The human kinome targeted by FDA approved multi-target drugs and combination products: A comparative study from the drug-target interaction network perspective|
YH Li, PP Wang, XX Li, CY Yu, H Yang, J Zhou, WW Xue, J Tan, F Zhu
PloS one 11 (11), e0165737, 2016
|Exploring the inhibitory mechanism of approved selective norepinephrine reuptake inhibitors and reboxetine enantiomers by molecular dynamics study|
G Zheng, W Xue, P Wang, F Yang, B Li, X Li, Y Li, X Yao, F Zhu
Scientific reports 6 (1), 26883, 2016
|Comparison of FDA approved kinase targets to clinical trial ones: insights from their system profiles and drug-target interaction networks|
J Xu, P Wang, H Yang, J Zhou, Y Li, X Li, W Xue, C Yu, Y Tian, F Zhu
BioMed Research International 2016, 2016
|Prediction of the binding mode and resistance profile for a dual-target pyrrolyl diketo acid scaffold against HIV-1 integrase and reverse-transcriptase-associated ribonuclease H|
F Yang, G Zheng, T Fu, X Li, G Tu, YH Li, X Yao, W Xue, F Zhu
Physical Chemistry Chemical Physics 20 (37), 23873-23884, 2018
|What makes species productive of anti-cancer drugs? Clues from drugs’ species origin, druglikeness, target and pathway|
X Li, X Li, Y Li, C Yu, W Xue, J Hu, B Li, P Wang, F Zhu
Anti-Cancer Agents in Medicinal Chemistry (Formerly Current Medicinal …, 2019