Research in our laboratory employs computer power to identify novel theoretical models that will aid in discovering new molecular entities, mastering difficult biological processes, and improving the computer-aided drug design process. It crosses the borders of many disciplines such as chemistry, biology, physics, and computer sciences, and so the ultimate goal is to employ the broad background acquired by our research team to unlock the mystery of challenging problems in the field of molecular modeling and simulation. The end result would be advancing the discovery of better therapeutic agents with higher efficacy, potency, and selectivity. Our research projects can be categorized into the following three major research areas.
First, utilizing state-of-the-art hardware and molecular modeling software to discover and optimize lead compounds. This area of research exploits structure-based and ligand-based drug design software tools developed in-house and by others to identify small molecules that can interfere with biological processes to provide pharmacological treatments. Such tools include pharmacophore modeling, QSAR studies, and docking, followed by lead optimization using bioisosteric replacement. In this realm, collaboration with experimentalists will be indispensable to produce a high-quality and successful research outcome.
Second, inspecting the association between structure, dynamics, and function of important drug targets using molecular dynamics simulation techniques. In this area of research group, molecular dynamics (MD) simulation is utilized to achieve mechanistic understanding for important biological processes at molecular level. MD simulation can shed light on the binding process of endogenous molecules to their targets; i.e., what are the conformational changes (induced by this binding) that triggers the signal transduction process. Collaboration with experimentalists is also indispensable in this realm as well, and their data are used to validate the simulation process. If the simulation model is valid, it can be used to provide answers and insights which will advance our understanding of such biological processes, and thus, support rational drug design and discovery process.
Third, developing efficient computer algorithms to solve cheminformatics and bioinformatics problems. This research area employs graph representation of native structures of molecules, macromolecules, or interfaces between them, followed by efficient subgraph mining, to identify frequent and common structural features (motifs) that can then be used to predict the structure or function of biomolecules. This approach was successfully used in the field of cheminformatics to develop molecular descriptors, identify common pharmacophoric groups, generate fragment-based virtual library, and extract ligand-receptor interaction patterns to assess in docking small molecules; a novel idea for which the CCG Excellence Award was granted. The same approach was also applied to solve bioinformatics problems as well; frequent geometric motifs of interfacial residues were extracted and used to assess in docking protein-protein complexes, and frequent geometric motifs of internal residues were extracted and used to assess in identifying correct protein folds.
