David Hecht

Adjunct Professor, Biochemistry

office: Southwestern Coll.
email: dhecht@swccd.edu
Hecht photo

Curriculum Vitae

  • B.S. , Biochemistry, Rutgers University, 1988
  • M.S., Chemistry, University of California, Berkeley, 1989
  • Ph.D., Macromolecular Structural Biology & Chemistry The Scripps Research Institute 1995
  • Research Scientist I, Corning Nichols Research Institute, 1995-1996
  • Scientist I & II, Signal Pharmacueticals, Inc., 1996-1999
  • Manager, Scientific Databases, Idun Pharmaceuticals, Inc., 2000-2005
  • Assistant and Full Professor, Southwestern College, 2005-present
  • Visiting Faculty, Asia University, Dept. of Medical Informatics, 2007-present
  • Adjunct Faculty, San Diego State University, Dept. of Chemistry, 2010-present

Research Interests

Infectious diseases represent a continuing and evolving threat despite decades of research and the elusive promise of disease eradication. Bacteria, viruses and other parasites have a tremendous opportunity for rapid evolution with large effective population sizes and high rates of mutation. As a result, the last decade has witnessed a re-emergence of bacteria and other parasites that have evolved resistance to many conventional antibiotics. These include Mycobacterium tuberculosis (tuberculosis); methicillin resistant strains of Staphylococcus aureus (MRSA); as well as Plasmodium falciparum (Malaria). New and improved methods that can quickly and more efficiently identify and evaluate new candidate drugs are urgently needed. My current research focuses on applications of computational intelligence in early stage drug discovery for diseases such as Malaria, Tuberculosis, MRSA, cancer and AIDS. Computational intelligence is a broad field in computer science focusing on the development of machine learning approaches for the automatic selection of features and optimization of models. The tools and techniques of this field include artificial neural networks, fuzzy logic, and evolutionary computation.

Selected Publications

  1. Hecht, D.; Wang, C.C.N.; Hu, R.-M; Tsai, J.J.P. Molecular Modeling Studies of AmpR Mediated AmpC B-Lactamase Repression. Proceedings of the 2011 11th IEEE International Conference on Bioinformatics and BioEngineering, BIBE 2011, Taichung, Taiwan (accepted, in press).

  2. Hecht, D.; Tran, J.; Fogel, GB. Structural-Based Analysis of Dihydrofolate Reductase Evolution. Molecular Phylogeny and Evolution, 2011, 61, 212-230.

  3. Hecht, D. Applications of Machine Learning and Computational Intelligence to Drug Discovery and Development. Drug Development Research, 2011, 72, 53-65.

  4. Fogel, G.B.; Tran, J.; Johnson, S.; Hecht, D. Machine Learning Approaches for Customized Docking Scores: Modeling of Inhibition of Mycobacterium tuberculosis Enoyl Acyl Carrier Protein Reductase. 2010 IEEE Computational Intelligence in Bioinformatics and Computational Biology, Montreal, 2010, 243-248.

  5. Hecht, D.; Sheu, P.C.Y.; Tsai, J.J.P., SCDL applications to drug discovery. Proceedings of the 2009 9th IEEE International Conference on Bioinformatics and BioEngineering, BIBE 2009 , art. no. 5211225, pp. 449-454.

  6. Wang, C.C.N.; Hecht D; Hsiao H.C.W.; Sheu PC-Y and Tsai JJP, Describing Dynamic Biological Systems in SPDL and SCDL. Proceedings of the 2009 9th IEEE International Conference on Bioinformatics and BioEngineering, BIBE 2009 , art. no. 5211117, pp. 455-460.

  7. Hecht, D.; Cheung M; Fogel GB, Docking Scores and QSAR Using Evolved Neural Networks for the Pan-Inhibition of Wild-type and Mutant PfDHFR by Cycloguanil Derivatives. 2009 IEEE Congress on Evolutionary Computation, Trondheim, Norway, 2009, 262-269.

  8. Hecht, D.; Fogel, G., A novel in silico approach to drug discovery via computational intelligence. J Chem Inf Model., 2009 ,49, 1105-21.

  9. Hecht, D.; Fogel, G., Computational Intelligence Methods for Docking Scores.Current Computer-Assisted Drug Design, 2009, 5, 56-68.

  10. Hecht, D.; Fogel, G., Computational Intelligence Methods for ADMET Prediction. in Frontiers in Drug Discovery and Development, Volume 4, Caldwell, G.W., Atta-ur-Rahman, Yan, Z., Choudhary, M.I. Editors.; Bentham Science Publishers, 2009, 351-377.

  11. Wang, S.; Hu, R.-M.; Hsiao H.C.W.; Hecht, D.; Ng, A.K.L.; Chen, R.-M.; Sheu, P.C.Y.; Tsai, J.P., Using SCDL For Integrating Tools And Data For Complex Biomedical Applications. International Journal of Semantic Computing, 2008, 2, 291-308..

  12. Hecht D; Hu R-M; Chen R-M; Ou J-W; Hsu C-Y; Gong H; Ng K-L; Hsiao HCW; Tsai JJP, and Sheu PC-Y, Biosemantic System: Applications Of Structured Natural Language To Biological And Biochemical Research. ASC2008 IEEE International Workshop on Ambient Semantic Computing, Taichung, Taiwan , 2008, 386-393.

  13. Davis N; Biddlecom N; Hecht D; Fogel GB, On the relationship between GC content and the number of predicted microRNA binding sites by MicroInspector. Computational Biology and Chemistry, 2008, 32, 222-226.

  14. Cheung M; Johnson, S.; Hecht D; and Fogel GB, Quantitative Structure-Property Relationships for Drug Solubility Prediction Using Evolved Neural Networks. 2008 IEEE Congress on Evolutionary Computation, Hong Kong, 2008, 688-693.

  15. Fogel GB; Cheung M; Pittman E; and Hecht D, In Silico Screening Against Wild-Type and Mutant Plasmodium falciparum Dihydrofolate Reductase. J.Molecular Graphics and Modelling, 2008, 26:1145-1152

  16. Fogel G.B.; Cheung M.; Pittman E.; Hecht D. Modeling the Inhibition of Quadruple Mutant Plasmodium falciparum Dihydrofolate Reductase by Pyrimethamine Derivatives.J. Comput. Aided Mol. Des. 2008, 22, 29-38.

  17. Hecht D; Cheung M.; Fogel G.B. QSAR Using Evolved Neural Networks for the Inhibition of Mutant PfDHFR by Pyrimethamine Derivatives. Biosystems, 2008, 92, 10-15.

  18. Hecht D and Fogel G.B. Evolved Neural Networks for High Throughput Ligand Screening. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2007, 4, 476-484.

  19. Zhang, X.; Hecht, D.; Sheu, P., Analyzing Chemical Compounds with ChemObjects. Integrated Design and Process Technology IDPT 2006, 2006.

  20. Ma CYC; Wong SWM; Hecht D.; Fogel G.B. Evolved Neural Networks for High Throughput Anti-HIV Ligand Screening. 2006 IEEE Congress on Evolutionary Computation, Vancouver, Canada, 2006, 9284-9291