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Faculty


Ra'ed Khashan

Associate Professor Pharmaceutical SciencesDivision of Pharmaceutical Sciences

Ph.D. Pharmaceutical Sciences, University of North Carolina at Chapel HillM.S. Pharmaceutical Sciences, The University of Texas at AustinB.S. Pharmacy, Jordan University of Science and Technology

http://www.khashanlab.org

Description

Dr. Ra’ed Khashan is a highly ambitious computational medicinal chemist who obtained his master’s & doctorate degrees at two of the top schools of pharmacy nationwide, UT-Austin and UNC-Chapel Hill, respectively. He has a broad, yet strong, background in pharmaceutical sciences, computer science and chemistry; as evidenced by his dual degree in Pharmacy and Computer Science with Minor in Chemistry.

He has 20+ years of experience in developing computational algorithms and machine learning methods, and applying data mining science, molecular modeling, and molecular dynamics simulation techniques to improve the efficiency of drug design and discovery process. His research assists medicinal chemists in understanding biochemical mechanisms, thus, facilitating the design of molecular entities that are more likely to be active. Dr. Khashan’s research is translational since it employs computer power to generate theoretical models that help conquer biochemical problems; the models are applied to develop trials and studies that directly benefits humans. It is an interdisciplinary research that crosses the boarders of many fields such as chemistry, biology, physics, mathematics, statistics, pharmaceutical, and computer sciences.

Dr. Khashan is a passionate teacher with a proven record for providing the best education to undergraduate, graduate, and PharmD students so they are prepared to serve the community and shine as true scientists. As a teacher, Dr. Khashan strives to cultivate an interactive setting where students can express themselves freely, be creative, and use critical thinking when solving problems. Every effort is made to use cutting-edge technological tools to help students. Concepts are usually explained very simply so students can understand rather than memorize, thus, they shall never forget. In addition to being a licensed Pharmacist with over ten years of experience in community pharmacy, Dr. Khashan connects concepts learned to their applications in real world. This helps Doctor of Pharmacy Students understand the concepts better, and clearly see their use in practice. Dr. Khashan has received several teaching awards including the American Association of Colleges of Pharmacy (AACP) Teacher of the Year Award.

Specialties

Computational Medicinal Chemistry, In-Silico Drug Design and Discovery, Artificial Intelligence and Machine Learning Data Science, Cheminformatics, Bioinformatics, Molecular Modeling, and Molecular Dynamics Simulation.

Publications

  • Fayyad A. AI & experimental-based discovery and preclinical IND-enabling studies of selective BMX inhibitors for development of cancer therapeutics. Int J Pharm. 2023. doi: 10.1016/j.ijpharm.2023.123384. PMID: 37678472.
  • Amnah Alalmaei, Saousen Diaf, and Raed Khashan. Insight into the Molecular Mechanism of the Transposon-encoded Type I-F CRISPR/Cas System. Journal of Genetic Engineering and Biotechnology, Vol. 21, Issue 60, pp. 1-15, 2023.
  • Faranak Dinehkaboudi, Nadimbhai Vahora, and Raed Khashan. Binding pocket identification and determination of overlapping with different software tools for V8 Protease (1QY6) from Staphylococcus aureus. Abstract and Poster at Discovery Day of Long Island University in Brooklyn, NY, April 25th, 2023.
  • Amnah Alalmaie and Raed Khashan. Mechanistic insight into the conformational changes of Cas8 upon binding to different PAM sequences in the transposon-encoded type I-F CRISPR-Cas system. Abstracts of Papers, ACS Spring National Meeting, Indianapolis, IN, March 26-30, 2023,COMP Poster Session.
  • Minkyung Kim, Ling Ni, and Raed Khashan. Novel drug discovery for COVID-19 using Computer Aided Drug Design (CADD) methods. Abstract and Poster at Center for Undergraduate Research (CUR) Annual Festival, Philadelphia, PA, March 21-24, 2022.
  • Amnah Alalmaie and Raed Khashan. Utilizing MD simulation to understand the molecular and structural mechanism underlying the DNA targeting by the INTEGRATE system. Abstracts of Papers, ACS Spring National Meeting, San Diego, CA, March 20-24, 2022,COMP Poster Session.
  • Khashan, R., Tropsha, A., and Zheng, W. Data Mining Meets Machine Learning: A Novel ANN-based Multi-Body Interaction Docking Scoring Function (MBI-Score) based on Utilizing Frequent Geometric and Chemical Patterns of Interfacial Atoms in Native Protein-Ligand Complexes. Molecular Informatics, Epub on Mar. 9, 2022. doi: 10.1002/minf.202100248. PMID: 35142086.
  • Khashan, R. Generating “Fragment-Based Virtual Library” Using Pocket Similarity Search of Ligand-Receptor Complexes. Chapter 3: Fragment-Based Methods in Drug Discovery, Methods in Molecular Biology. Edited by: Anthony E. Klon. 1289, 23-30 (2015).
  • Khashan, R., Zheng, W. and Tropsha, A. The Development of Novel Chemical Fragment-Based Descriptors Using Frequent Common Subgraph Mining Approach and Their Application in QSAR Modeling. Molecular Informatics, 33 (3), 201-215 (2014).
  • Khashan, R. FragVLib - A Free Program for Generating "Fragment-based Virtual Library" Using Pocket Similarity Search of Ligand-Receptor Complexes. Journal of Cheminformatics, 4 (1), 18 (2012).
  • Khashan, R., Zheng, W. and Tropsha, A. Scoring Protein Interaction Decoys using Exposed Residues (SPIDER): A Novel Multi-Body Interaction Scoring Function based on Frequent Geometric Patterns of Interfacial Residues. Proteins: Structure, Function, and Bioinformatics, 80(9), 2207-17 (2012).
  • Fleishman, S., Whitehead, T., Strauch, E., Khashan, R., Bush, S., Fouches, D., Tropsha, A., et al. Community-Wide Assessment of Protein-Interface Modeling Suggests Improvements to Design Methodology. Journal of Molecular Biology, 414 (2), 289-302 (2011).

Lectures and Presentations

  • Faranak Dinehkaboudi, Nadimbhai Vahora, and Raed Khashan. Binding pocket identification and determination of overlapping with different software tools for V8 Protease (1QY6) from Staphylococcus aureus. Abstract and Poster at Discovery Day of Long Island University in Brooklyn, NY, April 25th, 2023.
  • Amnah Alalmaie and Raed Khashan. Mechanistic insight into the conformational changes of Cas8 upon binding to different PAM sequences in the transposon-encoded type I-F CRISPR-Cas system. Abstracts of Papers, ACS Spring National Meeting, Indianapolis, IN, March 26-30, 2023, COMP Poster Session.
  • Minkyung Kim, Ling Ni, and Raed Khashan. Novel drug discovery for COVID-19 using Computer Aided Drug Design (CADD) methods. Abstract and Poster at Center for Undergraduate Research (CUR) Annual Festival of Spring 2022.
  • Amnah Alalmaie and Raed Khashan. Utilizing MD simulation to understand the molecular and structural mechanism underlying the DNA targeting by the INTEGRATE system. Abstract and Poster at ACS National Meeting of Spring 2022.
  • Raed Khashan, and Weifan Zheng. FragVLib: Fragment-based virtual library for in-silico screening using geometric and chemical patterns of interactions at interface of ligand-receptor complex crystal structures. Abstract and Poster at 240th ACS National Meeting of Fall 2010.
  • Raed Khashan, Weifan Zheng, and Alexander Tropsha. GeoIsosteres: Structure-based approach to finding bioisosteres using geometric and chemical patterns of interacting atoms at receptor-ligand interfaces. Abstract and Poster at 240th ACS National Meeting of Fall 2010.
  • Raed Khashan. Development of Ligand-based & Structure-based CADD Tools Using Frequent Subgraph Mining of Chemical Structures. Computer-Assisted Drug Design Group, Bristol-Myers Squibb Company – Lawrenceville, New Jersey, May 14th, 2008.
  • Raed Khashan. Development of Ligand-based & Structure-based CADD Tools Using Frequent Subgraph Mining of Chemical Structures. Computational Chemistry Group, Pfizer, Inc. – Groton, Connecticut, October 10th, 2007.
  • Raed Khashan, Weifan Zheng, Wei Wang, and Alexander Tropsha. Development of scoring functions for protein-ligand binding based on frequent geometric and chemical patterns of inter-atomic interactions at their interfaces. Abstract and Poster at 234th ACS National Meeting of Fall 2007.
  • Raed Khashan, Weifan Zheng, Wei Wang, and Alexander Tropsha. Development of docking protocols and scoring functions using frequent geometric and chemical patterns of inter-atomic interactions at the interface of protein-ligand complexes. Abstract and Poster at 233rd ACS National Meeting of Spring 2007.
  • Raed Khashan, Weifan Zheng, Jun Huan, Wei Wang, and Alexander Tropsha. Development of fragment-based chemical descriptors using novel frequent common subgraph mining approach and their application in QSAR modeling. Abstract and Poster at 230th ACS National Meeting of Fall 2005.
  • Scott Oloff, Raed Khashan, Robert Plourde, and Alexander Tropsha. Development of validated QSAR models of P2Y12 receptor antagonists and their application to database mining. Abstract and poster at 228th ACS meeting of Fall 2004.

Honors/Awards

Lindback Teaching Award (nomination), PCP College of Pharmacy, USciences – Philadelphia.

Bright Idea Teaching Award (nomination), PCP College of Pharmacy, USciences – Philadelphia.

PCPResearch & Scholar Award, PCP College of Pharmacy, USciences – Philadelphia.

AACP Teacher of the Year Award (nomination), College of Pharmacy, UT – Tyler.

AACP Teacher of the Year Award, College of Pharmacy, UT – Tyler.

Distinct Faculty Member Award, King Faisal University’s Deanship of Academic Development.

CCG Excellence Award, ACS’s Division of Computers in Chemistry.

UNC Excellence in Scholarship, UNC Graduate School Dissertation Fellow.

Texas Excellent Teaching Award (nomination), UT-Austin.

Honor List of distinguished students, Faculty of Science, Yarmouk University, Jordan.

Honor List of distinguished students, Faculty of Pharmacy, JUST University, Jordan.

Research Synopsis

Dr. Khashan’s laboratory employs computer power to identify novel theoretical models that will aid in mastering difficult biological processes, improving the computer-aided drug design process, and thus, facilitating the discovery of new molecular entities. 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 structural molecular pharmacology and medicinal chemistry. 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, machine learning tools, 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 and machine learning tools 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.