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Scientists at UNC-Chapel Hill led by Bryan L. Roth, MD, PhD, and colleagues from the University of Southern California and Northeastern University, have created and validated V-SYNTHES – a computational method to quickly screen billions of theoretical compounds for researchers to explore as potential therapies. Their work was published in Nature.

Bryan Roth peeks inside the PDSP robotic instrument

Bryan Roth, MD, PhD, teamed up with researchers from the University of Southern California and Northeastern University to validate V-SYNTHES, a new type of computational method developed by Vsevolod Katritch, PhD, at USC that allows scientists to first identify the best combinations of chemical building blocks called synthons – hypothetical units within molecules – to serve as seeds that can grow into a hierarchy of molecules with the best predicted ability to bind to the receptor targets.

“This approach allows researchers to computationally test billions of compounds against a therapeutic target,” Roth said. “To our knowledge this is the largest successful computational screen to date.”

As described in their Nature paper, they tested 11 billion theoretical compounds against a cannabinoid receptor (CB2) that marijuana’s active ingredient THC targets. The Katritch lab developed the approach and performed the computational studies. With help from the Northeastern Lab of Alexandros Makryannis, PhD, and Roth’s lab, the researchers discovered a new compound that is a highly selective and potent CB2 antagonist – a compound that dampens or blocks the activity of a receptor. Then the Makryannis and Roth labs validated this screening and discovery. They further validated this process against another target, the kinase ROCK1, a known drug target of many health conditions.

“V-SYNTHES represents a major advance in the field of drug discovery,” Roth said. “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.”

UNC School of Medicine authors are Yongfeng Liu, Xi-Ping Huang, Julie Picket, and Manish Jain. Other authors from USC are Blake Houser and Nilkanth Patel; from Northeastern are Alexandros Makriyannis, Christos Iliopoulos-Tsoutsouvas, Ngan Tran, Fei Tong, Nikolai Zvonok, and Spyros Nikas; from Enamine, Ltd. Olena Savych; from Enamine and Taras Shevchenko National University of Kyiv Dmytro Radchenko; and of Taras Shevchenko of National University of Kyiv and Chemspace LLC Yurii S. Moroz.

The study was funded by National Institute on Drug Abuse grants R01DA041435 and R01DA045020; National Institute of Mental Health grant R01MH112205 and Psychoactive Drug Screening Program; and National Institute of General Medical Sciences grant T32-GM118289.

~ the above is excepted from the original article of the same title by Mark Derewicz published on the UNC Health & SOM Newsroom Dec. 15, 2021, which explains in more detail why this new drug discovery tool is such an important development.

Nature Article Abstract

Structure-based virtual ligand screening is emerging as a key paradigm for early drug discovery owing to the availability of high-resolution target structures1,2,3,4 and ultra-large libraries of virtual compounds5,6. However, to keep pace with the rapid growth of virtual libraries, such as readily available for synthesis (REAL) combinatorial libraries7, new approaches to compound screening are needed8,9. Here we introduce a modular synthon-based approach—V-SYNTHES—to perform hierarchical structure-based screening of a REAL Space library of more than 11 billion compounds. V-SYNTHES first identifies the best scaffold–synthon combinations as seeds suitable for further growth, and then iteratively elaborates these seeds to select complete molecules with the best docking scores. This hierarchical combinatorial approach enables the rapid detection of the best-scoring compounds in the gigascale chemical space while performing docking of only a small fraction (<0.1%) of the library compounds. Chemical synthesis and experimental testing of novel cannabinoid antagonists predicted by V-SYNTHES demonstrated a 33% hit rate, including 14 submicromolar ligands, substantially improving over a standard virtual screening of the Enamine REAL diversity subset, which required approximately 100 times more computational resources. Synthesis of selected analogues of the best hits further improved potencies and affinities (best inhibitory constant (Ki) = 0.9 nM) and CB2/CB1 selectivity (50–200-fold). V-SYNTHES was also tested on a kinase target, ROCK1, further supporting its use for lead discovery. The approach is easily scalable for the rapid growth of combinatorial libraries and potentially adaptable to any docking algorithm.

Nature article link:  Nature (2021).