Demonstration of AutoDock as an Educational Tool for Drug Discovery

2017/02/13 by Travis R. Helgren, Timothy J. Hagen · 11 citations
Biochemistry, Genetics and Molecular Biology · Chemistry · Computer Science · Medicine · #AutoDock #Biochemistry #Biology #Cancer and biochemical research #Chemistry #Combinatorial chemistry #Computational Drug Discovery Methods #Computational biology #Computer science #Data science #Database #Docking (animal) #Drug discovery #In silico #Medicine #Various Chemistry Research Topics #Workflow

paper · doi:10.1021/acs.jchemed.6b00555

openalex publication_date 2017/02/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

Abstract

Drug design and discovery remains a popular topic of study to many students interested in visible, real-world applications of the chemical sciences. It is important that laboratory experiments detailing the early stages of drug discovery incorporate both compound design and an exploration of ligand/receptor interactions. Molecular modeling is widely employed in research endeavors seeking to predict the activity of potential compounds prior to synthesis and can therefore be used to illustrate these concepts. The following activity therefore details the use of AutoDock to predict the binding affinity and docked pose of a series of CDK2 inhibitors. Students can then compare their docking output to experimentally determined inhibitory activities and crystal structures. Finally, the AutoDock workflow detailed in this activity can be used in research settings, provided the receptor crystal structure is known.

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