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 () and Vsevolod Katritch ()
Additional contact information
Arman A. Sadybekov: University of Southern California
Anastasiia V. Sadybekov: University of Southern California
Yongfeng Liu: School of Medicine, University of North Carolina
Christos Iliopoulos-Tsoutsouvas: Northeastern University
Xi-Ping Huang: School of Medicine, University of North Carolina
Julie Pickett: School of Medicine, University of North Carolina
Blake Houser: University of Southern California
Nilkanth Patel: University of Southern California
Ngan K. Tran: Northeastern University
Fei Tong: Northeastern University
Nikolai Zvonok: Northeastern University
Manish K. Jain: School of Medicine, University of North Carolina
Olena Savych: Enamine Ltd
Dmytro S. Radchenko: Enamine Ltd
Spyros P. Nikas: Northeastern University
Nicos A. Petasis: University of Southern California
Yurii S. Moroz: Taras Shevchenko National University of Kyiv
Bryan L. Roth: School of Medicine, University of North Carolina
Alexandros Makriyannis: Northeastern University
Vsevolod Katritch: University of Southern California

Nature, 2022, vol. 601, issue 7893, 452-459

Abstract: Abstract Structure-based virtual ligand screening is emerging as a key paradigm for early drug discovery owing to the availability of high-resolution target structures1–4 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 (

Date: 2022
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DOI: 10.1038/s41586-021-04220-9

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