Ra’ed Khashan

Description

Dr. Khashan is a highly accomplished computational medicinal chemist, currently serving as an Associate Professor in the Division of Pharmaceutical Sciences at Long Island University. He holds a Ph.D. in Pharmaceutical Sciences from the University of North Carolina at Chapel Hill, where his dissertation focused on ligand and structure-based computational drug discovery using frequent subgraph mining. He also earned an M.S. in Pharmaceutical Sciences from the University of Texas at Austin, a B.S. in Computer Science with a minor in Chemistry from Yarmouk University, and a Bachelor of Pharmacy from Jordan University of Science and Technology. His multidisciplinary academic background—spanning pharmaceutical sciences, chemistry, and computer science—forms the foundation of his innovative research in developing and applying molecular modeling, simulation techniques, machine learning strategies, and computer-aided drug design tools to advance AI-driven and in silico drug discovery.

With over two decades of experience in academia and research, Dr. Khashan has developed a broad portfolio of projects at the intersection of cheminformatics, structural biology, and artificial intelligence. His research spans in-silico drug discovery, protein-ligand binding, molecular dynamics simulations, and fragment-based design methodologies. He has taught across numerous pharmacy and pharmaceutical sciences curricula, mentored Ph.D. and PharmD students, and contributed significantly to institutional service and grant development. He has actively pursued funding from federal agencies including NIH and NSF, with several successful awards from other sources supporting his work. His commitment to advancing computational drug discovery is reflected in his scholarly publications, educational innovation, and collaborative projects.

Research

• Computational Medicinal Chemistry; Molecular Modeling; Molecular Dynamics Simulation; In Silico Drug Discovery; AI & Machine Learning Data Science; Structure-based Drug Design

Dr. Khashan’s research focuses on the development and application of computational tools to accelerate and enhance drug discovery. His work integrates molecular modeling, cheminformatics, and machine learning to investigate biochemical interactions at the molecular level, particularly protein-ligand binding. He has pioneered techniques such as pocket similarity search for fragment-based drug design and developed novel scoring functions for docking and affinity prediction using graph mining and multi-body interaction models. His research also includes large-scale molecular dynamics simulations to uncover the structural mechanisms of drug targets, such as the insulin receptor and transposon-encoded CRISPR-Cas systems. Dr. Khashan’s lab actively applies these computational methods to generate virtual libraries, identify bioisosteres, and explore entropy-driven models for binding affinity, all with the goal of supporting rational drug design and improving therapeutic outcomes.

Distinctions & Awards

University of the Sciences

• PCP Research & Scholar Award, PCP College of Pharmacy, 2020

University of Texas at Tyler

• AACP Teacher of the Year Award, College of Pharmacy, 2015

King Faisal University

• Distinct Faculty Member Award, King Faisal University’s Deanship of Academic Development, 2012

American Chemical Society

• CCG Excellence Award, ACS’s Division of Computers in Chemistry, 2007

University of North Carolina at Chapel Hill

• UNC Excellence in Scholarship, UNC Graduate School Dissertation Fellow, 2006

Jordan University of Science and Technology

• Honor List of distinguished students, Faculty of Pharmacy, 1995

Yarmouk University

• Honor List of distinguished students, Faculty of Science, 1999

Selected Publications

• Ramadan A, Rao P, Allababidi S, Khashan R, Fathallah AM. Tolerization with a Novel Dual-Acting Liposomal Tim Agonist Prepares the Immune System for the Success of Gene Therapy. International Journal of Molecular Sciences. 2025 April; 26(8):3830. doi: 10.3390/ijms26083830.
• Amnah Alalmaie, 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. Proteins: Structure, Function, and Bioinformatics. 2024 Dec;92(12):1428-1448. doi: 10.1002/prot.26730. Epub 2024 Aug 22. PMID: 39171866.
• Elsanhoury R, Alasmari A, Parupathi P, Jumaa M, Al-Fayoumi S, Kumar A, Khashan R, Nazzal S, Abu 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.
• Raed Khashan. Chapter 3: Generating “Fragment-Based Virtual Library” Using Pocket Similarity Search of Ligand-Receptor Complexes. In: Fragment-Based Methods in Drug Discovery, Methods in Molecular Biology. Edited by: Anthony E. Klon. Vol. 1289, pp. 23-30, 2015.
• Raed Khashan, Weifan Zheng, and Alexander Tropsha. The Development of Novel Chemical Fragment-Based Descriptors Using Frequent Common Subgraph Mining Approach and Their Application in QSAR Modeling. Molecular Informatics, Vol. 33, Issue 3, pp. 201-215, 2014.
• Raed Khashan. FragVLib - A Free Program for Generating "Fragment-based Virtual Library" Using Pocket Similarity Search of Ligand-Receptor Complexes. Journal of Cheminformatics, Vol. 4, Issue 1, pp. 18, 2012.

Frequently-Cited Papers

• Raed Khashan, Alexander Tropsha, and Weifan Zheng. 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, Vol. 41, Issue 8, pp. 2100248-64, 2022. doi: 10.1002/minf.202100248. PMID: 35142086.
• Raed Khashan, Weifan Zheng, and Alexander Tropsha. 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, Vol. 80, Issue 9, pp. 2207-2217, 2012.
• Sarel J. Fleishman, Timothy A. Whitehead, Raed Khashan, Stephen Bush, Denis Fouches, Alexander Tropsha, et al. Community-Wide Assessment of Protein-Interface Modeling Suggests Improvements to Design Methodology. Journal of Molecular Biology, Vol. 414, Issue 2, pp. 289-302, 2011.