11 billion theoretical compounds tested with new computational method

The new approach allows researchers to computationally test billions of potential drug candidates' compatibility with target receptors.

Receptors cover the surface of every cell, which allows cells in our body to bind with an enormous variety of compounds, including pharmaceuticals and recreational drugs. Thirty percent of pharmaceutical drugs are designed to target G protein-coupled receptors, but these drugs often bind to other unintended receptors throughout the body, generating undesired side effects.

A drug needs to be designed to induce the intended chemical reaction in a cell while simultaneously not triggering unintended effects and not binding to other non-target receptors. All of these constraints make drug design rather tricky.

"What we need are more precise and less harmful treatments that are just as effective. In the past, scientists would test molecules in a one-by-one fashion against a therapeutic target in a lengthy and expensive operation," said Bryan L. Roth, MD, Ph.D., the Michael Hooker Distinguished Professor of Pharmacology at the UNC School of Medicine.

Roth and researchers from the University of Southern California and University of North Carolina at Chapel Hill collaborated to validate V-SYNTHES, a new computational method of testing theoretical compounds that could potentially bind with the desired affinity to a target receptor. It identifies synthons, hypothetical units within molecules, that serve as a starting point for a hierarchy of molecules with the highest predicted affinity. After identifying potential compounds, scientists can modify and produce a small amount to test in cell cultures.

V-SYNTHES was used to test 11 billion theoretical compounds against the CB2 cannabinoid receptor, which binds to THC found in cannabis. Researchers were able to identify and validate a new compound that is highly selective for the CB2 receptor and a potent antagonist, dampening or blocking its activity.

"V-SYNTHES represents a major advance in the field of drug discovery. It is easily scalable and adaptable, and it should open new vistas in the discovery of potentially therapeutic chemicals for a large number of disorders at a rate never before possible," said Roth."

You can read more from the team's paper, published in Nature, here.