• Home
  • Publications
  • Synthon-based ligand discovery in virtual libraries of over 11 billion compounds

Synthon-based ligand discovery in virtual libraries of over 11 billion compounds

Nature 2022, 601 (7893), 452-459

DOI: 10.1038/s41586-021-04220-9

Sadybekov A.; Sadybekov A.; Liu Y.; Iliopoulos-Tsoutsouvas C.; Huang X.; Pickett J.; Houser B.; Patel N.; Tran N.; Tong F.; Zvonok N.; Jain M.; Savych O.; Radchenko D.; Nikas S.; Petasis N.; Moroz Y. et al.

Structure-based virtual ligand screening is emerging as a key paradigm for early drug discovery owing to the availability of high-resolution target structures and ultra-large libraries of virtual compounds. However, to keep pace with the rapid growth of virtual libraries, such as readily available for synthesis (REAL) combinatorial libraries, new approaches to compound screening are needed. 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.