Modular synthon-based approach - V-SYNTHES was published in Nature 601, 452–459 (2022). It 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 method enables rapid detection of the best-scoring compounds in the ultra-large chemical space while performing molecular docking of only a small fraction (<0.1%) of the compounds.

The V-SYNTHES approach will be performed in a few stages:

1. We generate a library of fragment-like compounds representing all possible scaffold–synthon combinations for all reactions in the whole Enamine REAL Space (31 Billion molecules), which is referred to as a minimal enumeration Library (MEL).
2. The MEL compounds are docked onto the target receptor using energy-based docking of the flexible ligand. About 30 thousand of the top-scoring compounds will be used to apply the proprietary technology CapSelect. The technology will us allow to identify the preferable fragments for future growth into final molecules. The last step would be to filter the fragments for diversity using different criteria (i.e. a single reaction cannot contribute more than 20% of the selection).
3. The iterative enumeration of the selected fragments from stage 2.
4. The docking screen on the final enumerated subset of the library.

V-SYNTHES requires thousands of times less computational resources than standard VLS without compromising docking accuracy at any step. It was tested on Cannabinoid CB1/CB2, Kinase ROCK1, Angiotensin AT2, and Fungal Bromodomain and demonstrated a high hit rate, great potency, and affinity of the hits. The approach is easily scalable for the rapid growth of combinatorial libraries and potentially adaptable to any docking algorithm.

ICM-Pro is provided by MolSoft.

RDKit, KNIME

Synthon-based ligand discovery in virtual libraries of over 11 billion compounds. Arman A. Sadybekov, Anastasiia V. Sadybekov, Yongfeng Liu, Christos Iliopoulos-Tsoutsouvas, Xi-Ping Huang, Julie Pickett, Blake Houser, Nilkanth Patel, Ngan K. Tran, Fei Tong, Nikolai Zvonok, Manish K. Jain, Olena Savych, Dmytro S. Radchenko,
Spyros P. Nikas, Nicos A. Petasis, Yurii S. Moroz, Bryan L. Roth, Alexandros Makriyannis & Vsevolod Katritch. Nature 601, 452–459 (2022).

Virtual Ligand screen will be performed utilizing features of ICM-Pro.