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National University of Singapore, Science Faculty, Computational Sci Dept
 
   
   

Dr. Chen Yu Zong

Dr. Chen Yu Zong

Tenured professor

Department of Pharmacy, Faculty of Science, National University of Singapore
Blk S16, Level 8, 08-14, 2 Science Drive 2, Singapore 117543

Office: Blk S16 Room 08-14, Tel.: 65-6516-6877. Fax: 65-6774-6756.
E-mail: phacyz@nus.edu.sg
Web :
http://bidd.nus.edu.sg/group/bidd.htm

Opportunities for M.Sc. and Ph.D. studies and Postdoc positions in bioinformatics, modeling and drug-design.

Curriculum Vitae

Research interests:

  • Drug discovery: pharmainformatics, virtual screening, ADME-Tox prediction, target discovery, multi-target drugs
  • Computational biology: bioinformatics, systems biology, proteomics, biomarker discovery, immunology
  • Nano-science: Nano-systems simulation
  • Herbal medicine: herbal informatics, molecular mechanisms, combination therapies
  • Art and Science: digital art of proteins, protein music

Academic qualifications:

  • B.Sc. 1982 Dalian University of Technology, China
  • M.Sc. 1985 Institute of Theoretical Physics, Academia Sinica, China
  • Ph.D. 1989 University of Manchester, Institute of Science and Technology, U.K

Career history:

  • 2007 Jan - Present Tenured Professor, Dept. of Pharmacy, National Univ of Singapore
  • 2008 July – Present Member, International Scientific Committee, International Centre for Science & High Technology, UNIDO, Trieste, Italy
  • 2006 Jan – 2006 Dec Tenured Associate Professor, Dept. of Pharmacy, National Univ. of Singapore
  • 2004-Present Adjunct Professors, Qinghua Univ, Sichuan Univ, Xiamen Univ, SiChuan Univ, ChongQing Univ, Shanghai Center Bioinfo Tech
  • 2003-2005 Head, Dept of Computational Science, National Univ of Singapore
  • 2000-2005 Tenured Associate Professor, Dept of Computational Science, National Univ of Singapore
  • 2000-Present Fellow, Singapore-MIT alliance.
  • 1998-2000 Senior Lecturer, Dept of Computational Science, National Univ of Singapore
  • 1997-1998 Lecturer, Dept of Computational Science, National Univ of Singapore
  • 1997-1997 Research Scientist, ISIS Pharmaceuticals, Carlsbad, CA, USA
  • 1994-1996 Research Assistant Scientist, Biophysics Group, Dept of Phys, Purdue University, Indiana, USA
  • 1989-1993 Post-Doc Fellow, Biophysics Group, Dept of Phys, Purdue University, Indiana, USA

Major research accomplishments:

  • Pioneered inverse docking method for drug target discovery
  • Developed the popular therapeutic target database
  • Among World’s first in exploring machine learning methods for protein function prediction, ADME-Tox prediction, target discovery, multi-target virtual screening

Research output/impact indicators:

Publications:

  • H-Index 24, 16 invited reviews, 161 papers in international refereed journals
  • One therapeutic target database paper in Nucleic Acids Res featured in Journal’s editorial article
  • Two protein function prediction papers published in RNA and Proteins are on the Faculty of 1000 Biology list
  • Two papers published in Drug Discov Today and J Mol Graph Mod are on the journal 25 hottest articles list
  • One toxicity prediction paper published in Chem Res Toxicol is journal’s No 1 most cited paper in 2005
  • Three ADME-Tox prediction papers published in J Chem Info Mod are on the journal top 25 most cited list

Patents:

  • Target identification method
    (US Patent 6,519,611)
  • Biological pathway and molecular simulation system
    (U.S. Regular Patent Appl.10/674,586)
  • Herb/food effects and consumption information system
    (U.S. Provisional Appl. 60/512,479)

Software/database development:

Awards, honours, editorships, conference chairmanships, invited speakers:

  • Outstanding Scientist Award 2007, Science Faculty, National Univ of Singapore
  • Marquis Who’s Who in Science and Engineering. 7th, 8th, 9th edition 2003-2006; Marquis Who’s Who in Medicine and Health Care 6th edition 2006; Marquis Who’s Who in Asia 1st edition 2007
  • Member, International Scientific Committee, International Centre for Sci & High Tech, UNIDO, Trieste, Italy
  • Editorial board of Curr Proteomics, Prot Pept Lett, Bioinformation, Int J Integr Biol, J Pharmacol Pharm
  • Featured in “Protein art and music” Singapore Unified Morning News, 6/5/2005; “Protein music". Shanghai Evening News, 3/7/2005; “Computer study of Chinese medicine”. Singapore Unified Morning News, 19/8/2002
  • Invited podium speaker, PSWC 2007 Pharmaceutical Sciences World Congress, Amsterdam, 23 April 2007
  • Invited speaker, Symposium on Chem Vision in Life Science, KRICT, Korea, 25 August 2006
  • Invited speaker, ICS-UNIDO Workshop, Bangkok, 4-6 May 2009
  • Invited speaker, 3rd Asian Pacific ISSX regional meeting, Bangkok 10-12 May 2009
  • Invited speaker, 3rd Cross-Strait Theoretical and Computation Chemistry Conference, Chengdu 23-25 April 2009
  • Co-chair, platform session computational methods & molecular dynamics, 45th Annual Meeting of American Biophysical Society, Boston, USA. February 21, 2001
  • Organizer and chair, Minisymposium in math modeling in molecular biology and drug design, Pacific Rim Dynamics Systems Conferences, Hawaii, USA. 9-13 August, 2000
  • Invited speaker, AIMECS'99 International Medicinal Chemistry Symposium, Beijing, China. 13 Sept. 1999
  • Invited speaker, Annual meeting of American physical society, San Jose, USA. 24 March, 1995
  • Member, Singapore TCM Taskforce, Singapore Science Center TCM exhibition committee
  • Member, National Univ of Singapore Taskforces on Computational Biology, medicinal chemistry program development committee, life science curriculum committee, bioengineering program development committee

Funding:

PI of 11 Singapore ARF grants; 1 China NSF, 1 Hong Kong K.C.Wong grant; Co-PI of 1 China 863 grant

Teaching:

Graduate courses taught:

Computer aided drug design, Molecular modelling, Bioinformatics, Computational biology, Biotechnology, Simulation Methods, Biophysics

Undergraduate courses taught:

Computer aided drug design, Medicinal chemistry, Computational chemistry, Bioinformatics, Simulations, Parallel and Distributed Computing, Computational physics, Computational Science (On-line lecture notes adopted by Education Curriculum Center, The Mathworks, UK in 2004-2006)

Representative publications (all as the sole corresponding author):

1. What are next generation innovative therapeutic targets? Clues from genetic, structural, physicochemical and systems profile of successful targets. F. Zhu, L.Y. Han, … Y.Z. Chen. J Pharmacol Exp Ther. 330:304-15(2009)
2. Synergistic therapeutic actions of herbal ingredients and their mechanisms from molecular interaction and network perspectives X. H. Ma, C.J. Zheng, … Y. Z. Chen. Drug Discov Today. 14:579-588(2009).
3. Mechanisms of drug combinations from interaction and network perspectives J. Jia, F. Zhu, X.H. Ma, Z.W. Cao, Y.X. Li and Y.Z. Chen. Nature Rev. Drug Discov., 8(2):111-28(2009)
4. A support vector machines approach for virtual screening of active compounds of single and multiple mechanisms from large libraries at an improved hit-rate and enrichment factor. L.Y. Han, X.H. Ma, …, Y.Z. Chen. J Mol Graph Model 26(8):1276-1286 (2008)
5. Derivation of Stable Microarray Cancer-differentiating Signatures by a Feature-selection Method Incorporating Consensus Scoring of Multiple Random Sampling and Gene-Ranking Consistency Evaluation. Z.Q. Tang, L.Y. Han, H.H. Lin, J. Cui, J. Jia, B.C. Low, B.W. Li, Y.Z. Chen. Cancer Res. 67:9996-10003 (2007).
6. Support vector machine approach for predicting druggable proteins: Recent progress in its exploration and investigation of its usefulness. L.Y. Han, , …, and Y.Z. Chen. Drug Discov Today 12: 304-313 (2007)
7. PharmGED: Pharmacogenetic Effect Database B. Xie,…, and Y. Z. Chen, Clin. Pharmacol. Ther. 81: 29 (2007)
8. Therapeutic Targets: Progress of Their Exploration and Investigation of Their Characteristics. C.J. Zheng, L.Y. Han, C. W. Yap, Z. L. Ji, Z. W. Cao and Y. Z. Chen. Pharmacological Reviews 58, 259-279 (2006)
9. Prediction of p-glycoprotein substrates by support vector machine approach. Xue, Y.; Yap, C. W.; Sun, L. Z.; Cao, Z. W.; Wang, J. F.; Chen, Y. Z. J. Chem. Inf. Comput. Sci. 44, 1497-505 (2004)
10. SVM-Prot: Web-Based Support Vector Machine Software for Functional Classification of a Protein from Its Primary Sequence. C.Z. Cai, L.Y. Han, Z.L. Ji, X. Chen, Y.Z. Chen. Nucleic. Acids Res. 31: 3692-3697 (2003)
11. TTD: Therapeutic Target Database. X. Chen, Z.L. Ji, and Y. Z. Chen, Nucleic. Acids. Res., 30, 412 (2002)
12. Ligand-Protein Inverse Docking and Its Potential Use in Computer Search of Putative Protein Targets of a Small Molecule. Y. Z. Chen and D. G. Zhi, Proteins, 43, 217 (2001)

Selected publications:

Drug Discovery (all but one as the sole corresponding author):

Pharmainformatics:
1. PharmGED: Pharmacogenetic Effect Database B. Xie,…, and Y. Z. Chen, Clin. Pharmacol. Ther. 81: 29 (2007).
2. PEARLS: Program for Energetic Analysis of Receptor-Ligand System. L.Y. Han, H.H. Lin, Z. R. Li, C.J. Zheng, Z.W. Cao, B. Xie, and Y. Z. Chen. J. Chem. Inf. Model. 23:445-450 (2006)
3. DART: Drug Adverse Reaction Target Database. Z. L. Ji, L. Y. Han, C. W. Yap, L. Z. Sun, X. Chen, and Y Z. Chen. Drug Safety 26, 685-690 (2003).
4. Absorption, distribution, metabolism, and excretion-associated protein database. L. Z. Sun, Z. L. Ji, X. Chen, J. F. Wang, and Y. Z. Chen,, Clin. Pharmacol. Ther. , 71, 405 (2002).

Virtual screening and ADME-Tox prediction:
1. Virtual Screening of Abl Inhibitors from Large Compound Libraries by Support Vector Machines. X.H. Liu, X.H. Ma, C.Y. Tan, Y.Y. Jiang, M.L. Go, B.C. Low and Y.Z. Chen. J Chem Info Model 2009 (accepted)
2. Comparative analysis of machine learning methods in ligand-based virtual screening of large compound libraries. X.H. Ma, J. Jia, F. Zhu, …and Y. Z. Chen. Comb. Chem. High Throughput Screen. 12(4):344-357(2009).
3. Evaluation of Virtual Screening Performance of Support Vector Machines Trained by Sparsely Distributed Active Compounds. X.H. Ma, R. Wang, S.Y. Yang, … and Y. Z. Chen .J Chem Inf Model. 48(6):1227-1237 (2008)
4. A support vector machines approach for virtual screening of active compounds of single and multiple mechanisms from large libraries at an improved hit-rate and enrichment factor. L.Y. Han, X.H. Ma, …, Y.Z. Chen. J Mol Graph Model 26(8):1276-1286 (2008)
5. Machine Learning Approaches for Predicting Compounds That Interact with Therapeutic and ADMET Related Proteins. H. Li, C.W. Yap, …and Y.Z. Chen. J. Pharm. Sci. 96:2838-2860 (2007).
6. In Silico Prediction of Pregnane X Receptor Activators by Machine Learning Approaches. C.Y. Ung, H. Li, C.W. Yap and Y.Z. Chen. Mol. Pharmacol. 71:158-168 (2007).
7. Formulation Development of Transdermal Dosage Forms: Quantitative Structure Activity Relationship Model for Predicting Activities of Terpenes that Enhance Drug Penetration Through Human Skin. L. Kang, C.W. Yap, PFC Lim, Y.Z. Chen, P C L Ho, YW Chan, GP Wong and S Y Chan. J. Controlled Release 120:211-219 (2007)
8. Classification of a Diverse Set of Tetrahymena Pyriformis Toxicity Chemical Compounds from Molecular Descriptors by Statistical Learning Methods Y. Xue, ..and Y.Z. Chen. Chem. Res. Toxicol. 19: 1030-1039 (2006).
9. Effect of Selection of Molecular Descriptors on the Prediction of Blood-Brain Barrier Penetrating and Non-penetrating Agents by Statistical Learning Methods. H. Li, C. W. Yap, C. Y. Ung,Y. Xue, Z. W. Cao, and Y. Z. Chen. J. Chem. Inf. Model. 45: 1376-1384 (2005)..
10. Prediction of Cytochrome P450 3A4, 2D6, 2C9 Inhibitors and Substrates by Using Support Vector Machines. C.W. Yap, Y.Z. Chen J. Chem. Inf. Model. 45: 982-992 (2005).
11. Prediction of Genotoxicity of Chemical Compounds by Statistical Learning Methods. H. Li, Y. Xue, C.Y. Ung, C.W. Yap, Z.R Li, and Y.Z. Chen. Chem Res Toxicol. 18:1071-1080 (2005).
12. Effect of molecular descriptor feature selection in support vector machine classification of pharmacokinetic and toxicological properties of chemical agents. Xue, Y.; Li, Z…..; Chen, Y. Z. J. Chem. Inf. Comput. Sci. 44: 1630-1638(2004)
13. Prediction of torsade-causing potential of drugs by support vector machine approach. Yap, C. W., Cai, C. Z., Xue, Y., and Chen, Y. Z. Toxicol. Sci. 79: 170-177 (2004).

Drug combinations and multi-targeting:
1. Synergistic therapeutic actions of herbal ingredients and their mechanisms from molecular interaction and network perspectives X. H. Ma, C.J. Zheng, … Y. Z. Chen. Drug Discov Today. 14:579-588(2009).
2. Mechanisms of drug combinations: interaction and network perspectives J. Jia, F. Zhu, X.H. Ma, Z.W. Cao, Y.X. Li and Y.Z. Chen. Nature Rev. Drug Discov., 8(2):111-28(2009)

Target discovery:
1. What are next generation innovative therapeutic targets? Clues from genetic, structural, physicochemical and systems profile of successful targets. F. Zhu, L.Y. Han, … Y.Z. Chen. J Pharmacol Exp Ther. 330:304-15(2009)
2. Support vector machine approach for predicting druggable proteins: Recent progress in its exploration and investigation of its usefulness. L.Y. Han, , …, and Y.Z. Chen. Drug Discov. Today 12: 304-313 (2007).
3. Computer prediction of drug resistance mutations in proteins. Z. W. Cao, L. Y. Han, C. J. Zheng, Z. L. Ji, X. Chen, H. H. Lin and Y. Z. Chen Drug Discov. Today 10:521-529 (2005)
4. Ligand-Protein Inverse Docking and Its Potential Use in Computer Search of Putative Protein Targets of a Small Molecule. Y. Z. Chen and D. G. Zhi, Proteins, 43, 217 (2001).
5. Prediction of Potential Toxicity and Side Effect Protein Targets of a Small Molecule by a Ligand-Protein Inverse Docking Approach. Y. Z. Chen, C. Y. Ung, J. Mol. Graph. Mod., 20, 199-218 (2001).

Computational Biology (all but two as the sole corresponding author, one as co-corresponding author):

Systems biology, biomarker discovery, proteomics:
1. Pathway sensitivity analysis for detecting pro-proliferation activities of oncogenes and tumor suppressors of EGFR-ERK pathway at altered protein levels H. Li, C. Y. Ung, … Y. Z. Chen. Cancer. 2009 (accepted)
2. Simulation of Crosstalk between Small GTPase RhoA and EGFR-ERK Signaling Pathway via MEKK1. H. Li, C. Y. Ung, X. H. Ma, B. W. Li, B. C. Low, Z. W. Cao and Y. Z. Chen.Bioinformatics.25(3):358-64(2009)
3. Simulation of the Regulation of EGFR Endocytosis and EGFR-ERK Signaling by Endophilin-Mediated RhoA-EGFR Crosstalk. C.Y. Ung, H. Li, …, B.C. Low and Y.Z. Chen. FEBS Lett. 582:2283-2290 (2008)
4. Derivation of Stable Microarray Cancer-differentiating Signatures by a Feature-selection Method Incorporating Consensus Scoring of Multiple Random Sampling and Gene-Ranking Consistency Evaluation. Z.Q. Tang, L.Y. Han, H.H. Lin, J. Cui, J. Jia, B.C. Low, B.W. Li, Y.Z. Chen. Cancer Res. 67:9996-10003 (2007).
5. Advances in exploration of machine learning methods for predicting functional class and interaction profiles of proteins and peptides irrespective of sequence homology J. Cui, L.Y. Han, H.H. Lin, Z.Q. Tang, Z.L. Ji, Z.W. Cao, Y.X. Li, and Y.Z. Chen. Curr. Bioinformatics 2: 95-112 (2007).
6. Effect of training datasets on support vector machine prediction of protein-protein interactions. S.L. Lo, C. Z. Cai, Y.Z. Chen and Maxey C. M. Chung. Proteomics 5:876-884 (2005)

Bioinformatics:
1. PROFEAT: A Web Server for Computing Structural and Physicochemical Features of Proteins and Peptides from Amino Acid Sequence. Z.R. Li, H.H. Lin, L.Y. Han, … and Y.Z. Chen. Nucleic Acids Res. 34, W32-7 (2006)
2. MoViES: Molecular Vibrations Evaluation Server for Analysis of Fluctuational Dynamics of Proteins and Nucleic Acids. Z.W. Cao, Y. Xue, …, and Y. Z. Chen, Nucleic. Acids Res. 32. W679-W685 (2004)
3. TRMP: A Database of Therapeutically Relevant Multiple-Pathways. C.J.Zheng, H. Zhou, B. Xie, L.Y. Han, C.W. Yap, and Y. Z. Chen, Bioinformatics. 20:2236-41(2004)
4. KDBI: Kinetic Data of Bio-molecular Interactions Database. Z. L. Ji, X. Chen, …, and Y. Z. Chen. Nucleic. Acids. Res. 31: 255-257 (2003).
5. ADME-AP: A database of ADME associated proteins. L. Z. Sun, Z. L. Ji, X. Chen, J. F. Wang, and Y. Z. Chen. Bioinformatics, 18:1699-1700 (2002).

Protein function:
1. Prediction of the Functional Class of Lipid-Binding Proteins from Sequence Derived Properties Irrespective of Sequence Similarity. H.H. Lin, L.Y. Han, … , and Y.Z. Chen. J. Lipid Res. 47(4):824-31 (2006)
2. Prediction of Transporter Family by Support Vector Machine Approach H. H. Lin, L.Y. Han, C.Z. Cai, Z. L. Ji, and Y.Z. Chen. Proteins. 62 (1): 218-31 (2006)
3. Prediction of Functional Class of the SARS Coronavirus Proteins by a Statistical Learning Method.C.Z. Cai, L.Y. Han, X. Chen, Z.W. Cao, Y.Z. Chen. J. Proteome Res. 4 (5): 1855-1862 (2005).
4. Prediction of Functional Class of Novel Viral Proteins by a Statistical Learning Method Irrespective of Sequence Similarity. L.Y.Han, C.Z Cai, Z. L. Ji, Y.Z. Chen. Virology 33:136-143(2005)
5. Predicting Functional Family of Novel Enzymes Irrespective of Sequence Similarity: A Statistical Learning Approach. L.Y.Han, C.Z.Cai, Z.L.Ji, Z.W.Cao, J.Cui, Y.Z.Chen. Nucleic Acids Res. 32: 6437-6444(2004)
6. Enzyme Family Classification by Support Vector Machines. C.Z. Cai, …, Y.Z. Chen. Proteins. 55,66-76 (2004).
7. Prediction of RNA-Binding Proteins from Primary Sequence by Support Vector Machine Approach. L.Y. Han, C.Z. Cai, S. L. Lo, Maxey C. M. Chung, Y. Z. Chen. RNA. 10: 355-368. (2004).

Immunology:
1. Genome-Scale Search of Tumor-Specific Antigens by Collective Analysis of Mutations, Expressions and T-Cell Recognition. J. Jia, Cui. J. , … Y. Z. Chen. Mol. Immunol. 46:1824-1829(2009).
2. AAIR: Antibody Antigen Information Resource. Z.Q. Tang, …, Y.Z. Chen. J. Immunol. 178: 4705 (2007)
3. Prediction of MHC-Binding Peptides of Flexible Lengths from Sequence-Derived Structural and Physicochemical Properties. J. Cui, L. Y. Han, …, and Y. Z. Chen. Mol. Immunol. 44: 866-877 (2007).
4. Computer Prediction of Allergen Proteins from Sequence-Derived Protein Structural and Physicochemical Properties J. Cui, L.Y. Han, …, and Y.Z. Chen . Mol. Immunol. 44: 514-520 (2007).
5. MHC-BPS: MHC-Binder Prediction Server for Identifying Peptides of Flexible Lengths from Sequence-Derived Physicochemical Properties. J. Cui, L.Y. Han, …, and Y.Z. Chen Immunogenetics 58:607-13 (2006)

Biomolecular Modelling:
1. Correlation between Normal Modes in The 20-200cm-1 Frequency Range and Localized Torsion Motions Related to Certain Collective Motions in Proteins. Z. W. Cao, …and Y. Z. Chen. J. Mol. Graph. Mod. 21,309-319 (2003).
2. Spontaneous base flipping in DNA and its possible role in methyltransferase binding. Y.Z. Chen, V. Mohan, and R. H. Griffey, Phys. Rev. E62, 1133-1137 (2000).
3. Effect of backbone zeta torsion angle on low energy single base opening in B-DNA crystal structures. Y.Z. Chen, V. Mohan, and R.H. Griffey, Chem. Phys. Lett. 287, 570 (1998)
4. Theory of DNA melting based on the Peyrard-Bishop model. Y.L. Zhang, W.M. Zheng, J.X. Liu, Y.Z. Chen, Phys. Rev. E56, 7100-7115 (1997).
5. Premelting base pair opening probability and drug binding constant of a daunomycin--Poly d(GCAT)-Poly d(ATGC) complex. Y.Z. Chen and E.W. Prohofsky, Biophys. J. 66, 820 (1994).
6. The role of a minor groove spine of hydration in stabilizing Poly(dA)-Poly(dT) against fluctuational interbase H-bond disruption in the premelting temperature regime. Y.Z. Chen & E.W. Prohofsky, Nucleic. Acids. Res. 20, 415 (1992)
7. Energy flow considerations and thermal fluctuational opening of DNA base pairs at a replicating fork: Unwinding consistent with observed replication rates. Y.Z. Chen, W. Zhuang & E.W. Prohofsky, J. Biomol. Struct. Dynam. 10, 415 (1992).

Nano-science:

1. Simulation of DNA Electrophoresis in Systems of Large Number of Solvent Particles by Coarse-Grained Hybrid Molecular Dynamics Approach. R. Wang, J.S. Wang, … Y. Z. Chen. J Comput Chem. 30(4):505-13(2009).
2. Realistic simulations of combined DNA electrophoretic flow and EOF in nano-fluidic devices. D Duong-Hong, JS Wang, G.R. Liu, Y.Z. Chen J.Y. Han, and N.G. Hadjiconstantinou. Electrophoresis 29, 4880 (2008)
3. Dissipative particle dynamics simulations of electroosmotic flow in nano-fluidic devices. D Duong-Hong, JS Wang, G.R. Liu, Y.Z. Chen J.Y. Han, and N.G. Hadjiconstantinou. Microfluid. Nanofluid. 4, 219 (2008)
4. Continuum transport model of Ogston sieving in patterned nanofilter arrays for separation of rod-like biomolecules. ZR Li, G.R. Liu, Y.Z. Chen, J.S. Wang, …, Y Cheng, and J.Y. Han. Electrophoresis 29, 329 (2008)

Herbal Medicine (all but one as the sole corresponding author, one as the joint corresponding author):

1. Synergistic therapeutic actions of herbal ingredients and their mechanisms from molecular interaction and network perspectives X. H. Ma, C.J. Zheng, … Y. Z. Chen. Drug Discov Today. 2009 (accepted).
2. Are Herb-Pairs of Traditional Chinese Medicine Distinguishable from Others? Pattern Analysis and Artificial Intelligence Classification Study of Traditionally-Defined Herbal Properties. C.Y. Ung, … and Y.Z. Chen. J. Ethnopharmacol. 111:371-377 (2007)
3. Database of traditional Chinese medicine and its application to studies of mechanism and to prescription validation. X Chen, H Zhou, … and YZ Chen Br. J. Pharmacol. 149:1092-1103 (2006).
4. Usefulness of Traditionally-Defined Herbal Properties for Distinguishing Prescriptions of Traditional Chinese Medicine from Non-Prescription Recipes C.Y. Ung, … and Y.Z. Chen. J. Ethnopharmacol. 109: 21-28 (2006).
5. Traditional Chinese Medicine Information Database. Z. L. Ji, H. Zhou, J. F. Wang, L. Y. Han, C. J. Zheng, and Y. Z. Chen. J. Ethnopharmacol. 103:501 (2006)..
6. TCM-ID: Traditional Chinese Medicine information database. J. F. Wang, H. Zhou, L. Y. Han, C.J. Zheng, C.Y. Kong, C.Y. Ung, H. Li, Z.W. Cao , X. Chen and Y. Z. Chen, Clin Pharmacol. Ther. 78:92-93 (2005).
7. A Computer Method for Validating Traditional Chinese Medicine Herbal Prescriptions. J. F. Wang, C. Z. Cai1, C. Y. Kong, and Y. Z. Chen. Am. J. Chin. Med. 33:281-297(2005).
8. Computer Automated Prediction of Putative Therapeutic and Toxicity Protein Targets of Bioactive Compounds from Chinese Medicinal Plants. Y. Z. Chen and C. Y. Ung, Am. J. Chin. Med., 30, 139 (2002).

Invited Reviews (all as the sole corresponding author):

1. Trends in the Exploration of Anticancer Targets and Strategies in Enhancing the Efficacy of Drug Targeting. F. Zhu, C.J. Zheng, L.Y. Han, … Y.Z. Chen. Curr Mol Pharmacol. 1(3):213-232(2008)
2. Advances in Machine Learning Prediction of Toxicological Properties and Adverse Drug Reactions of Pharmaceutical Agents. X.H. Ma, …, Y.Q. Wei and Y.Z. Chen. Current Drug Safety. 3(2):100-114 (2008).
3. Advances in exploration of machine learning methods for predicting functional class and interaction profiles of proteins and peptides irrespective of sequence homology J. Cui, L.Y. Han, …, and Y.Z. Chen. Curr. Bioinformatics 2: 95-112 (2007).
4. Progress and Problems in the Exploration of Therapeutic Targets. C.J. Zheng, L.Y. Han, C. W. Yap, B. Xie, and Y. Z. Chen Drug Discovery Today 11: 412-420 (2006).
5. Information of ADME-associated proteins and potential application for pharmacogenetic prediction of drug responses. C.J. Zheng, L.Y. Han, ,… and Y. Z. Chen. Curr. Pharmacogenomics 4:87-103 (2006).
6. Recent progresses in the application of machine learning approach for predicting protein functional class independent of sequence similarity. L.Y. Han, J. Cui, …, and Y.Z. Chen Proteomics. 6: 4023-4037 (2006).
7. Application of Support Vector Machines to in silico Prediction of Cytochrome P450 Enzyme Substrates and Inhibitors. C. W. Yap, Y. Xue, Z. R. Li, and Y. Z. Chen Curr. Top. Med. Chem. 6:1593-1607 (2006)
8. Prediction of Compounds with Specific Pharmacodynamic, Pharmacokinetic or Toxicological Property by Statistical Learning Methods. C. W. Yap, Y. Xue…, and Y. Z. Chen. Mini. Rev. Med. Chem. 6:449-459 (2006).
9. Computer prediction of drug resistance mutations in proteins, Z. W. Cao, L. Y. Han, C. J. Zheng, Z. L. Ji, and Y. Z. Chen. Drug Discovery Today, 10:521-529 (2005)
10. Trends in Exploration of Therapeutic Targets. C.J. Zheng, L.Y. Han, C. W. Yap, B. Xie, and Y. Z. Chen, Drug News & Perspectives 18:109-127 (2005)
13. Drug ADME-Associated Protein Database as a Resource for Facilitating Pharmacogenomics Research. C.J. Zheng, L. Z. Sun, L. Y. Han, Z. L. Ji, X. Chen, and Y. Z. Chen. Drug Dev. Res. 62:134–142 (2004).
14. Internet Resources for Proteins Associated with Drug Therapeutic Effects, Adverse Reactions, and ADME. Z. L. Ji, L. Z. Sun, X. Chen, …, and Y. Z. Chen, Drug Discovery Today, 8,526-529. (2003).
15. Can an In-Silico Drug-Target Search Method be Used to Probe Potential Mechanisms of Medicinal Plant Ingredients? X. Chen, C. Y. Ung, and Y. Z. Chen. Nat. Prod. Rep. 20: 432-444 (2003).

PhDs trained:

Student Name: Guo Yong Jian
Year of PhD award: 2000 (MSc)
Research Field: Computational Biology
Current Position: Lead Bioinformatics Developer, NIAID, NIH, USA

Student Name: Cao Zhi Wei
Year of PhD award: 2004
Research Field: Bioinformatics
Current Position: Professor, Assist Dean, Tongji University, China

Student Name: Ji Zhi Liang
Year of PhD award: 2004
Research Field: Bioinformatics, Computer aided drug design
Current Position: Professor, Deputy Department Head, XiaMen Univ, China


Student Name: Chen Xin
Year of PhD award: 2004
Research Field: Bioinformatics, Computer aided drug design
Current Position: Associate Professor, Deputy Head of Department, Zhejiang Univ, China

Student Name: Yap Chun Wei
Year of PhD award: 2006
Research Field: Computer aided drug design
Current Position: Assistant Professor, National Univ of Singapore

Student Name: Han Lian Yi
Year of PhD award: 2006
Research Field: Bioinformatics, Computer aided drug design
Current Position: Staff Scientist, Pubchem, NCBI, NIH, USA

Student Name: Zheng Chan Juan
Year of PhD award: 2006
Research Field: Computer aided drug design, Bioinformatics
Current Position: Research Fellow, CDD, CBB, NCBI, NIH, USA

Student Name: Lin Hong Huang
Year of PhD award: 2007
Research Field: Bioinformatics, Computer aided drug design
Current Position: Research Assistant Professor, Boston Univ, USA

Student Name: Li Hu
Year of PhD award: 2007
Research Field: Computer aided drug design, Bioinformatics
Current Position: Research Fellow, Boston University, USA

Student Name: Cui Juan
Year of PhD award: 2008
Research Field: Bioinformatics
Current Position: Research Associate, Univ of Giorgia, USA

Student Name: Tan Zhi Qun
Year of PhD award: 2008
Research Field: Bioinformatics
Current Position: Research Associate, George Town Univ, USA

Student Name: Ung Choong Yong
Year of PhD award: 2008
Research Field: Computer aided drug design, Bioinformatics
Current Position: Research Associate, National University of Singapore

Student Name: Zhang Hai Lei
Year of PhD award: 2008
Research Field: Bioinformatics
Current Position: Research Associate, Harvard Univ Medical School, USA

Student Name: Pankaj Kumar
Year of PhD award: 2009
Research Field: Computer aided drug design, Bioinformatics
Current Position: Research Fellow, IMCB Singapore

Student Name: Liu Xiang Hui
Year of PhD award: 2010
Research Field: Computer aided drug design
Current Position: Research Fellow, Tan Soo Shen Hospital, Singapore

Statistics of publications in 2001-2010:

Journal name, Impact factor, No of papers published

  • Nature Reviews Drug Discovery, 23.308, 1
  • Cancer Research, 7.672, 1
  • Pharmacological Reviews, 18.823, 1
  • Nucleic Acids Research, 7.479, 9
  • Clinical Pharmacology & Therapeutics, 8.033, 3
  • Physical Review Letters, 7.489, 1
  • Drug Discovery Today, 6.761, 5
  • Journal of Proteome Research, 5.675, 1
  • Natural Product Reports, 7.325, 1
  • Journal of Immunology, 6.068, 1
  • Journal of Controled Release, 5.949, 1
  • RNA, 6.145, 1
  • Drug Metabolism Reviews, 5.754, 1
  • New Phytologist, 6.033, 1
  • Molecular Pharmaceutics, 5.408, 1
  • Proteomics, 5.479, 3
  • J Comput Chem, 5.817, 4
  • Cancer, 5.418, 1
  • Molecular Pharmacology, 4.088, 1
  • AIDS, 5.334, 1
  • Current Topics in Medicinal Chemistry, 4.400, 1
  • Bioinformatics, 5.039, 4
  • Journal of Pharmacology & Exp Ther, 4.006, 1
  • BMC Bioinformatics, 3.493, 2
  • British Journal of Pharmacology, 3.767, 1
  • Proteins, 3.354, 3
  • Journal of Chemical Info & Comp Science, 3.882, 5
  • Molecular Immunology, 3.742, 3
  • Toxicological Science, 3.367, 1
  • Journal of Lipid Research, 4.336, 1
  • Chemical Research in Toxicology, 3.508, 2
  • Virology, 3.080, 1
  • Drug Safety, 3.673, 1
  • Immunogenetics, 2.741, 1
  • J Mol Catal A Chem, 3.135, 1
  • Biopolymers, 2.545, 1
  • QSAR Comb Sci, 3.027, 1
  • Physical Review E, 2.010, 9
  • Journal of Molecular Graphics & Modeling, 1.932, 5
  • Journal of Pharmaceutical Science, 2.942, 2

Publications with Higher Number of Citations (H-index: 24, Total No of SCI Citations: 2,069)

  • Mechanisms of drug combinations: interaction and network perspectives Nat. Rev. Drug Discov., 8(2):111-28(2009). No of citations: 16
  • A support vector machines approach for virtual screening of active compounds of single and multiple mechanisms from large libraries at an improved hit-rate and enrichment factor. J Mol Graph Mod. 26(8):1276-1286 (2008). No of citations: 16
  • Prediction of MHC-Binding Peptides of Flexible Lengths from Sequence-Derived Structural and Physicochemical Properties. Mol. Immunol. 44: 866-877 (2007). No of citations: 24
  • In Silico Prediction of Pregnane X Receptor Activators by Machine Learning Approaches. Mol. Pharmacol. 71(1):158-168 (2007). No of citations: 27
  • PROFEAT: A Web Server for Computing Structural and Physicochemical Features of Proteins and Peptides from Amino Acid Sequence. Nucleic Acids Res.Jul 1;34(Web Server issue):W32-7 (2006). No of citations: 25
  • Recent progresses in the application of machine learning approach for predicting protein functional class independent of sequence similarity. Proteomics. 6: 4023-4037 (2006). No of citations: 26
  • Prediction of Transporter Family by Support Vector Machine Approach. Proteins. 62 (1): 218-31 (2006). No of citations: 29
  • Therapeutic Targets: Progress of Their Exploration and Investigation of Their Characteristics. Pharmacol. Rev. 58:259-279 (2006). No of citations: 48
  • Effect of Selection of Molecular Descriptors on the Prediction of Blood-Brain Barrier Penetrating and Non-penetrating Agents by Statistical Learning Methods. J. Chem. Inf. Model. 45: 1376-1384 (2005). No of citations: 49
  • Quantitative structure-pharmacokinetic relationships for drug distribution properties by using general regression neural network. J Pharm Sci 94:153-168 (2005). No of citations: 24
  • Prediction of Genotoxicity of Chemical Compounds by Statistical Learning Methods. Chem Res Toxicol.18, 1071-1080 (2005). No of citations: 36
  • Prediction of Cytochrome P450 3A4, 2D6, 2C9 Inhibitors and Substrates by Using Support Vector Machines. J. Chem. Inf. Model. 45: 982-992 (2005). No of citations: 58
  • Drug bioactivation, covalent binding to target proteins and toxicity relevance. Drug Metab. Rev. 31, 41-213 (2005). No of citations: 76
  • Predicting Functional Family of Novel Enzymes Irrespective of Sequence Similarity: A Statistical Learning Approach. Nucleic Acids Res.32: 6437-6444(2004). No of citations: 43
  • Effect of molecular descriptor feature selection in support vector machine classification of pharmacokinetic and toxicological properties of chemical agents. Chem. Inf. Comput. Sci. 44,1630 (2004). No of citations: 68
  • Prediction of RNA-Binding Proteins from Primary Sequence by Support Vector Machine Approach. RNA. 10, 355-368. (2004). No of citations: 41
  • Prediction of P-glycoprotein substrates by a support vector machine approach, J. Chem. Info. & Comp Sci. 44, 1497 (2004). No of citations: 82
  • Prediction of torsade-causing potential of drugs by support vector machine approach. Toxicol. Sci. 79(1),170-177. (2004). No of citations: 36
  • Enzyme family classification by support vector machines, Proteins 55, 66 (2004). No of citations: 64
  • Support Vector Machine Classification of Physical and Biological Datasets. Inter.J.Mod.Phys.C 14(5),575 - 585. (2003). No of citations: 23
  • Protein function classification via support vector machine approach. Math Biosci, 185, 111-122 (2003). No of citations: 48
  • SVM-Prot: web-based support vector machine software for functional classification of a protein from its primary sequence, Nucleic Acids Res. 31, 3692 (2003). No of citations: 117
  • TTD: Therapeutic Target Database. Nucleic. Acids. Res. 30, 412-415 (2002) 56
    Inhibition of epidermal growth factor receptor (EGFR) tyrosine kinase by chalcone derivatives. BBA: Prot. Struct. Mol. Enzym. 1550, 144-152 (2001). No of citations: 30
  • Prediction of potential toxicity and side effect protein targets of a small molecule by a ligand-protein inverse docking approach, J. Mol. Graph. Model. 20, 199 (2001). No of citations: 33
  • Ligand-protein inverse docking and its potential use in the computer search of protein targets of a small molecule, Proteins 43, 217 (2001). No of citations: 77
  • Theory of DNA melting based on the Peyrard-Bishop model, Phys. Rev. E 56, 7100 (1997). No of citations: 50
  • Differences in melting behavior between homopolymers and copolymers of DNA: Role of non-bonded forces for GC and the role of the hydration spine and premelting transition for AT. Biopolymers33, 797 (1993). No of citations: 26
  • The role of a minor groove spine of hydration in stabilizing Poly(dA)-Poly(dT) against fluctuational interbase H-bond disruption in the premelting temperature regime. Nucleic. Acids. Res. 20, 415 (1992). No of citations: 33


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