The majority of our recent and current research involves graph representation of native structures of molecules, macromolecules, or interfaces between them, followed by efficient subgraph mining. This will identify frequent and common structural features (motifs) that can then be used to predict the structure or function of biomolecules. We applied this approach successfully to the field of Cheminformatics to develop molecular descriptors,1-3 identify common pharmacophoric groups,3 generate fragment-based virtual library,4 and extract ligand-receptor interaction patterns to assess in docking small molecules;5 a novel idea for which the CCG Excellence Award was granted.6 We are also working on developing docking scoring functions for protein-ligand complexes that take care of the inaccuracy issues resulting from the lack of entropy contribution.

Subgraph mining have also been applied to solve Bioinformatics problems as well. We extracted frequent geometric motifs of interfacial residues to assess in docking protein-protein complexes (SPIDER).7,8 We also extracted frequent geometric motifs of internal residues to assess in identifying correct protein folds.9 Additionally, we applied subgraph mining to identify 3D-structural motifs of the residues responsible for determining the function of the protein. As a result, having the 3D crystal structure of a protein it should be possible to know its biological function.

On the other hand, we use Molecular Dynamics (MD) Simulation to achieve mechanistic understanding for important biological process at molecular level. For example, we use MD simulation to 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. In this realm, collaboration with experimentalists will be indispensable; we will use their data to validate the simulation process, and if valid, we can use the simulation model to provide answers and insights which will advance our understanding of such biological processes, and thus, support rational drug design and discovery process.


  1. R Khashan*. Refine & Valid of BCUT Descriptors for Computer Assisted Drug Discovery. Thesis (M.S. in Pharmacy) — University of Texas at Austin, August 2003.
  2. R Khashan*, W Zheng, and A Tropsha. The Development of Novel Chemical Fragment-Based Descriptors Using Frequent Common Subgraph Mining Approach and Their Application in QSAR Modeling. Molecular Informatics, Volume 33, Issue 3, pp. 201-215, 2014.
  3. R Khashan*. Develop & App of Ligand/Structure-based Comp Drug Discovery Tools Based on Freq Subgraph Mining of Chemical Structures. Dissertation (Ph.D. in Pharmacy) — University of North Carolina at Chapel Hill, August 2007.
  4. R Khashan*. FragVLib – A Free Prog for Generating “Frag-based Virtual Lib” Using Pocket Similarity Search of Ligand-Receptor Comp. J Cheminformatics, 4 (1), 18, 2012.
  5. R Khashan*, et al. A Novel Multi-Body Interaction (MBI) Statistical Pose-Scoring Function based on Frequent Geometric & Chemical Patterns of Interacting Atoms in Native Protein-Ligand Complexes. Submitted.
  6. R Khashan wins CCG Excellence Award, ACS’s Division of Computers in Chemistry, Spring 2007.
  7. R Khashan, et al. Scoring Protein Interation Decoys using Experimental Residues (SPIDER): A Novel Multi-Body Interaction Scoring Function based on Frequent Geometric Patterns of Interfacial Residues. Proteins, 80 (9), 2207-2217, 2012.
  8. S Fleishman, T Whitehead, R Khashan, A Tropsha, et al. Community-Wide Assess of Protein-Interface Modeling Sugg Improv to Design Meth. J Mol Bio, 414 (2), 289-302, 2011.
  9. R Khashan*. SCORPIONS: Scoring Protein Folds using 3D-Structural Motifs of Internal Residues found in Native Protein Structures. In Preparation.