Abstract
Traditional methods of drug discovery can be time-intensive and have significant costs. The process of finding an effective drug is often nonlinear involving heuristics, chemical intuition, and several rounds of trial and error. In comparison, structure-based drug design has emerged as a tool to develop drugs for specific protein targets, which can shorten the time to market and reduce costs. The program OpenGrowth, developed by the Shakhnovich Biophysics Lab at Harvard University, is a particularly promising computational approach for de novo drug design. This algorithm combines various chemical molecules, or fragments, to create a suitable drug in the context of a protein binding site. We ran this program to find a drug that can bind to the protein HIV-1 protease and thus inhibit the normal function of this protein, which is critical for the life cycle of the HIV virus. We produced 500 potential drugs, 42.5% of which passed the OpenGrowth 3Mer test assessing if analogs of the drug substructure have been found in other compounds. We then evaluated the drugs for ADME (absorption, distribution, metabolism, and excretion) properties using the program SwissADME; toxicity with the program ProTox-II; and similarity using the Tanimoto coefficient. Overall, we were able to determine that OpenGrowth is able to search chemical space and find drugs that bind and fit into a particular binding site. Additional tests will be necessary to determine the efficacy of these drugs in an in vitro or in vivo environment.
How to Cite:
, M. & , C.,
(2023) “De novo drug design for a protein binding site ”,
Journal of Student-Scientists' Research 5.
doi: https://doi.org/10.13021/jssr2023.3925
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