Work

Comparing Neural-Network Scoring Functions and the State of the Art: Applications to Common Library Screening

Year: 2013

Type: article

Abstract: We compare established docking programs, AutoDock Vina and Schrödinger's Glide, to the recently published NNScore scoring functions. As expected, the best protocol to use in a virtual-screening project is highly dependent on the target receptor being studied. However, the mean screening performance obtained when candidate ligands are docked with Vina and rescored with NNScore 1.0 is not statistically different than the mean performance obtained when docking and scoring with Glide. We further demonstrate that the Vina and NNScore docking scores both correlate with chemical properties like small-molecule size and polarizability. Compensating for these potential biases leads to improvements in virtual screen performance. Composite NNScore-based scoring functions suited to a specific receptor further improve performance. We are hopeful that the current study will prove useful for those interested in computer-aided drug design.

Source: Journal of Chemical Information and Modeling

DOI: 10.1021/ci400042y

Authors: Jacob D. Durrant, Aaron J. Friedman, Kathleen E. Rogers, J. Andrew McCammon

Institutions: University of California San Diego, Howard Hughes Medical Institute, Center for Theoretical Biological Physics

Language: English

Topic: Computational Drug Discovery Methods

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $4,500

Funders: Howard Hughes Medical Institute, National Institutes of Health, National Institute of General Medical Sciences