Research Interests

Chen Yu Zong

Department of Pharmacy, National University of Singapore

I. Outline:

My research interests are in the areas of computer-aided drug design, computational biology and bioinformatics. These are interdisciplinary areas still in development and grow rapidly along with advances in life scince, computational techniques, and computer technology. The objective of my research is to develop innovative software tools and databases to facilitate new drug discovery. I have also been doing basic research in computational biology.

Life science is an information intensive science. As a result, computer tools and IT technology are expected to play a key role in the research and development of drugs as well as other fields in biological and medical sciences. This combination of information technology into biotechnology is both challenging and rewarding.

Biocomputing research is strongly interdisciplinary. As a result, collaboration with researchers in different fields is essential to success. We have been actively collaborating with researchers from Dept. of Pharmacy NUS (Study of neutoactive agents), Bioinformatics Center NUS (bioinformatics research and teaching), Shanghai Institute of Materia Medica Chinese Academy of Science (Chinese natural product research), Dept. of Physics and Astronomy University of Toledo (vibrational motions in biomolecules), Dept. of Physics Purdue University (modeling of the dynamics of DNA), Dept. of Physics Dalian University of Technology (mathematical models of biomolecular motions). In addition, we are initiating new collaborations with Institute of Biomaterials and bioengineering Tokyo Medical and Dental University (DNA alkylating drugs), Beijing Institute of Materia Medica Chinese Academy of Medical Sciences (Drug discovery), and Dept. of Physics, ChongQing University (biophysics).

We have close ties with industry. For instance we have on-going projects with ISIS Pharmaceuticals (USA) on antisense drug design. We are also discussing potential collaboration with Origenix Technologies (Canada) on antibacterial drugs, and Chengdu DiAo Pharmaceuticals (China) on Chinese natural product drugs.

Our research has led to one patent and 17 international journal publications since 1997. The total number of my publications stands at 43 journal publications + 8 conference proceedings.

II. My current research projects:

III. Other ongoing collaborative projects:

• Chinese natural product research. Shanghai Institute of Materia Medica,
• Chinese Academy of Sciences, China.
• RNA dynamics and antisense drug design. ISIS Pharmaceuticals, USA.
• Vibrational motions in biomolecules. Prof. S.A. Lee, Dept. of Physics and Astronomy, University of Toledo, USA.
• Mathematical models of biomolecular motions. Prof. H.S. Song, Dept. of Physics, Dalian University of Technology, China.

IV.Representative publications(all as the sole corresponding author):

1. 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., 2008 (accepted)
2. 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).
3. 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 Discovery Today 12: 304-313 (2007)
4. PharmGED: Pharmacogenetic Effect Database B. Xie,..., and Y. Z. Chen, Clin. Pharmacol. Ther. 81: 29 (2007)
5. 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)
6. 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)
7. TTD: Therapeutic Target Database. X. Chen, Z.L. Ji, and Y. Z. Chen, Nucleic. Acids. Res., 30, 412 (2002)
8. 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)

V. Selected publications:

Computer Aided Drug Design (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. TTD: Therapeutic Target Database. X. Chen, Z.L. Ji, and Y. Z. Chen, Nucleic. Acids. Res., 30, 412 (2002).
5. 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. 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 2008 (accepted)
2. 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).
3. 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).
4. 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)
5. 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).
6. 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)..
7. 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).
8. 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).
9. Quantitative structure-pharmacokinetic relationships for drug distribution properties by using general regression neural network. C. W. Yap, Y.Z. Chen. J Pharm. Sci. 94:153-168 (2005).
10. 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)
11. 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).
12. 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).

Target discovery:

1. 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 Discovery Today 12: 304-313 (2007).
2. 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)
3. 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).
4. 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):

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. 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)
6. 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).

Microarray biomarker discovery, proteomics, and systems biology:

1. Simulation of the Regulation of EGFR Endocytosis and EGFR-ERK Signaling by Endophilin-Mediated RhoA-EGFR Crosstalk. C.Y. Ung, H. Li, X.H. Ma, J. Jia, B.W. Li, B.C. Low and Y.Z. Chen. FEBS Lett.2008 (accepted)
2. 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).
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, 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).
4. 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)

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. AAIR: Antibody Antigen Information Resource. Z.Q. Tang, ¡­, Y.Z. Chen. J. Immunol. 178: 4705 (2007)
2. 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).
3. 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).
4. 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 Modeling

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. Modified self-consistent harmonic approach to thermal fluctuational disruption of disulfide bonds in proteins. Y.Z. Chen, Phys. Rev. E60, 5938-5942 (1999)
4. 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)
5. 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).
6. 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).
7. 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)
8. 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).

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

1. 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)
2. 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).
3. 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).
4. 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)..
5. 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).
6. 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).
7. 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):

• Support vector machine approach for predicting druggable proteins: Recent progress in its exploarion and investigation of its usefulness. L.Y. Han,... and Y.Z. Chen. Drug Discovery Today 12: 304-313 (2007).
• 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).
• 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)
• 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).
• 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).
• 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).
• 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)
• 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).
• 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)
• 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)
• 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¨C142 (2004).
• 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).
• 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).

VI. Invited talks

International Conferences

• Calculation of DNA-drug binding constant. March meeting of American physical society, San Jose, USA. Mar.24, 1995.
• Ligand-Protein Docking and Rational Drug Design. International Medicinal Chemistry Symposium. Beijing, China. September 12-15, 1999.
• A new computer method for drug target search and application to probing molecular mechanism of Chinese natural products. The Second International Symposium for Chinese Medicinal Chemists. Chengdu, China. Ocotober 15-19, 2000.

• Protein Dynamics. Joint APBioNet-BIC BioMolecular Structural Analysis Workshop 1998. National University of Singapore, Singapore. March 26, 1998.
• Molecular Modelling of Drug Resistance. Joint APBioNet-BIC 2nd Annual BioMolecular Structural Analysis Workshop 1999. National University of Singapore, Singapore. April 27, 1999.
• Computer simulations of biomolecular motions. Joint APBioNet-BIC 2nd Annual BioMolecular Structural Analysis Workshop 1999. National University of Singapore, Singapore. April 27, 1999.
• Computation of Hydrogen Bond Disruption Free Energy in Proteins. BioInformatics 21, 4th Annual Symposium of Bioinformatics Center, National University of Singapore. 26th - 27th Oct, 1999.
• Finding Multiple Protein Targets of a Ligand by Computer. 5th Annual Symposium of BioInformatics Centre. National University of Singapore. 5 - 6 October 2000.