My ultimate goal is to constuct a bridge between bioinformatics and computer-aided drug design (CADD)
1. Computer-aided drug design. Application of molecular docking, 2D/3D-QSAR, pharmacophore modeling, virtual screening and other molecular modeling techniques for development of new drugs. My research goal is to identify novel potential drugs by using molecular modeling techniques in conjunction with experimental assays.
2. Software development. Development of the general data structures and template libraries suitable for both computational programs and user interfaces. My research goal is to develop a universal programming environment for molecular input/output, atom typing definition and molecular operations.
3. Protein-ligand interactions. Development of methods of protein/ligand docking and scoring functions used in virtual screening. My research interest is to develop efficient molecular docking procedure and relatively precise scoring functions to estimate the binding between receptor and drugs.
4. ADME/Tox prediction. Development of prediction models for ADME/Tox predictions by using statistical and machine learning methods. My goal is to develop an integrated intelligent system to predict the ADME/Tox properties, including logP, pKa, logD, solubility, Caco-2 permeability, logBB, intestinal absorption, bioavailability, CYP450 metabolism and toxicity by using artificial neutral networks, support vector machine, Bayesin networks, graphics information and etc.
5. Domain-peptide interactions. Determining the binding specificity and protein interacting partners of peptide-recognition protein domains by using binding free energy calculations and bioinformatics analysis. My ultimate goal is to construct a protein interaction network in which each human modular domain (such as SH2, SH3, WW, PDZ, and etc.) is linked to its cognate partners.
6. Transcriptional networks. Development of methods to identify the regulatory element recognized by each transcription factor in the yeast genome and study the cooperation between transcription factors (combinatorial regulation) in a condition-dependent manner.
