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Jan 24, 2013
 |  BC Innovations  |  Cover Story

In silico drug design

Researchers at the University of Dundee and The University of North Carolina at Chapel Hill School of Medicine have created a computational algorithm that mines medicinal chemistry literature to predict new ligands that bind specific combinations of G protein-coupled receptors.1Ex Scientia Ltd. was spun out of Dundee to commercialize the findings and already has two deals in place related to the screening technology.

Developing a small molecule that binds a predefined combination of targets is at best a time-consuming medicinal chemistry effort and often has proven flat-out impossible.

One major obstacle to designing such drugs is the limited human capacity to sift through the vast accumulation of medicinal chemistry data related to multiple targets and then identify an optimal solution.

A team led by Andrew Hopkins and Bryan Roth reasoned that a computational algorithm might be able to more effectively undertake medicinal chemistry design than a person could and thus could better identify ligands with predetermined polypharmacology, which is the modulation of multiple targets.

Hopkins is chair of medicinal informatics and professor of translational biology at Dundee and founder and managing director of Ex Scientia. Roth is a professor in the Department of Pharmacology at the UNC at Chapel Hill School of Medicine.

First, their team mined data in the ChEMBL public database, which contains compound and activity data extracted from decades of published medicinal chemistry literature. The group used this data to build Bayesian models of ligand activity across 784 human protein targets, including G protein-coupled receptors (GPCRs).

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