The search for compounds that could be used as effective medicines for terrible diseases takes a lot of time, money, and effort – and it frequently fails.
V-SYNTHES is a computational method developed and validated by scientists at UNC-Chapel Hill, led by Bryan L. Roth, MD, PhD, and colleagues from the University of Southern California and Northeastern University, to quickly screen billions of theoretical compounds for researchers to investigate as potential therapies. Their findings were published in the journal Nature.
Bryan L. Roth, MD, PhD, the Michael Hooker Distinguished Professor of Pharmacology at the UNC School of Medicine said “What we need are more precise and less harmful treatments that are just as effective,” However, developing improved medications is a difficult task. To achieve the exact chemical reaction we want inside cells, drug developers need to know the particular chemical structure of a medication and the intended receptor. The catch is that the medicine must not impact other receptors or attach to target receptors in such a way that it causes unintended consequences inside cells.
“Unfortunately,” Roth added, “chemical space is vast.” “It has been estimated that there exists, theoretically, more chemicals than there are actual molecules in the universe. And only a small sliver of the potential chemicals can be physically tested.”
Together with researchers from the University of Southern California and Northeastern University, Roth validated V-SYNTHES, a new type of computational method developed by Vsevolod Katritch, PhD, at USC. Through V-SYNTHES, scientists can first identify the combinations of chemical building which are among the best and then serve them as seeds that can grow into a hierarchy of molecules with the best predicted ability to bind to the receptor targets.
This method enables researchers to evaluate billions of chemicals computationally against a medicinal target. This was the largest successful computational screen to yet, according to Roth.
V-SYNTHES, according to Roth, is a significant advancement in the field of drug discovery since it is easily scalable and flexible, and it should open up new vistas in the discovery of potentially therapeutic compounds for a wide range of illnesses at a rate never seen before.
