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Dr.
Chen Yu Zong
Tenured
professor
Department
of Pharmacy, Faculty
of Science, National University
of Singapore
Blk MD1, Level 5, 05-03H, 12 Science Drive 2, Singapore
117549
Office:
Blk MD1 Room 05-03H, 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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