headshot photo of Dr. Gennady Verkhivker

Dr. Gennady Verkhivker

Professor
Schmid College of Science and Technology; Computational and Data Science
School of Pharmacy
Office Location: Hashinger Science Center 118
Phone: (714) 516-4586
Scholarly Works:
Digital Commons
Education:
Lomonosov Moscow State University, Bachelor of Science
Lomonosov Moscow State University, Master of Science

Biography

Dr. Verkhivker is currently a Professor of Computational Biology. He is also an Adjunct Professor of Pharmacology at the Department of Pharmacology, UC San Diego. His research activities are in the areas of computational cancer biology, translational bioinformatics, and computational pharmacology. He received his PhD in Physical Chemistry from Moscow University and completed a postdoctoral fellowship in computational biophysics from University of Illinois at Chicago. In 2000-2005, Dr. Verkhivker has held various research and management positions at Pfizer Global Research and Development, San Diego.  Since 2002, he has been Adjunct Professor of Pharmacology at the Skaggs School of Pharmacy and Pharmaceutical Sciences, UC San Diego. In 2006, he joined School of Pharmacy and Center for Bioinformatics, The University of Kansas as a Professor of Pharmaceutical Chemistry and Bioinformatics. Dr. Verkhivker returned to California in 2011 and joined Chapman University in August 2011 as a Full Professor of Computational Biology. Upon arrival to Chapman, Dr. Verkhivker has established a dynamic research group engaged in translational research that attracted a large cohort of undergraduate and graduate students.  He has been involved in various global University initiatives and collaborations. Dr. Verkhivker has served on a number of important committees including Search Committee for new Found Dean of BioPharmacy School. He is also chairing the Doctoral Steering Committee of the newly approved PhD Graduate Program in Computational Sciences.

Recent Creative, Scholarly Work and Publications

Verkhivker GM, Agajanian S, Oztas DY, Gupta G. Atomistic Simulations and In Silico Mutational Profiling of Protein Stability and Binding in the SARS-CoV-2 Spike Protein Complexes with Nanobodies: Molecular Determinants of Mutational Escape Mechanisms, ACS Omega, 2021, in press
Verkhivker GM, Agajanian S, Oztas DY, Gupta G. Allosteric Control of Structural Mimicry and Mutational Escape in the SARS-CoV-2 Spike Protein Complexes with the ACE2 Decoys and Miniprotein Inhibitors: A Network-Based Approach for Mutational Profiling of Binding and Signaling. J. Chem. Inf. Model. 2021, in press
Verkhivker G. Computational Modeling and Engineering of Allosteric Regulatory Mechanisms in Signaling Proteins: Integration of Multiscale Simulations, Network Biology and Machine Learning. Biophysical journal. 2020 February; 118(3):206A. doi: https://doi.org/10.1016/j.bpj.2019.11.1238.
Generative Machine Learning Models for Discovery of selective chemical probes to interrogate protein kinase mechanisms. Agajanian S, Oluyemi O, Verkhivker GM. Nature Communications, Submitted 2019