Mohamed Elrefaiy
Computational Chemist
Interested in molecular modeling and drug discovery through advanced computational methods
Core Expertise
Quantum Mechanics
Advanced quantum mechanical calculations for molecular systems, including DFT and ab initio methods for accurate electronic structure predictions.
Molecular Dynamics
Expertise in MD simulations using GROMACS and AMBER for studying protein dynamics, drug-target interactions, and binding mechanisms.
Drug Discovery
Computational approaches for drug design including virtual screening, binding free energy calculations, and structure-activity relationships.
Photosynthetic Systems
Specialized in modeling light-harvesting complexes, energy transfer mechanisms, and electronic properties of photosynthetic proteins.
Scientific Computing
Proficient in Python, high-performance computing, and developing computational tools for scientific research and data analysis.
Data Analysis
Advanced statistical analysis, machine learning applications, and visualization of complex molecular and chemical datasets.
Research Highlights
Explore my key research areas and computational contributions
Hamiltonian Construction
High-throughput algorithms for building structure-based Hamiltonians in pigment-protein complexes with optimized electrostatic models.
Developed computational methods that achieve comparable accuracy to quantum mechanical calculations while requiring substantially fewer computational resources. Applied to multiple photosynthetic systems including PSII, WSCP, and IsiA complexes.
Drug Design & Development
Computational drug design using molecular Dynamics simulations and binding free energy calculations for therapeutic compound evaluation.
Performed comprehensive drug discovery research using advanced molecular simulation techniques. Established structure-function relationships and developed predictive models for drug-target interactions with applications in antitumor and riboswitch inhibitor design.
Get in Touch
Interested in collaborating or learning more about my research?
I'd love to hear from you!