# Efficient synthon-based modular screening of Giga-to-Terra-scale virtual libraries

> **NIH NIH R01** · UNIVERSITY OF SOUTHERN CALIFORNIA · 2023 · $412,500

## Abstract

ABSTRACT
The goal of our proposal is to develop a scalable platform for structure-based virtual screening of Giga- and Tera-
scale drug-like compound libraries, enabling streamlined discovery of high-quality drug candidates. Availability
of protein target structures and Giga-scale REAL Space libraries of virtual compounds (>10 billion) position
docking-based virtual screening as a key paradigm for drug discovery. However, the computational cost of Giga-
scale screening becomes a major bottleneck limiting further growth of the screening libraries. Recently, we have
introduced a highly scalable synthon-based technology, V-SYNTHES, which performs hierarchical structure-
based screening of REadily AvaiLable for synthesis (REAL) libraries (Sadybekov et al, Nature accepted).
By iteratively screening synthon-scaffold combinations, the V-SYNTHES approach makes possible rapid
detection of the best-scoring compounds in the Giga-scale chemical space while performing docking of only a
small fraction (~2 million) of the library. First tests of V-SYNTHES demonstrated strong enrichment in
computational benchmarks and significantly improved experimental hit rates on cannabinoid receptor CB2 and
ROCK1 kinase targets, while requiring 100 times less computational resources than standard virtual screenings.
Building upon these preliminary results, our proposal aims to: (1) Further develop a fully automated V-
SYNTHES algorithm, optimize its parameters and expand it to Tera-scale REAL libraries. (2) Apply and
experimentally validate the V-SYNTHES approach on a set of therapeutic targets of different classes, which
includes such challenging targets as nucleotide and lipid binding pockets, allosteric pockets, and orphan
receptors (3) Establish portability of the algorithm to an open-source docking platform to further facilitate V-
SYNTHES adoption in academic labs. The open-source algorithm will be distributed as a workflow for Linux
clusters and computing clouds. Successful completion of this project will establish V-SYNTHES as a robust
computational platform for structure-based ligand discovery in most classes of therapeutic targets, scaleable for
rapidly growing REAL modular libraries. Most importantly, it will help to make fast virtual screening of the
Giga-to-Tera-scale libraries broadly accessible for the whole research community with reasonable computational
resources.

## Key facts

- **NIH application ID:** 10710170
- **Project number:** 5R01GM147537-02
- **Recipient organization:** UNIVERSITY OF SOUTHERN CALIFORNIA
- **Principal Investigator:** VSEVOLOD KATRITCH
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $412,500
- **Award type:** 5
- **Project period:** 2022-09-26 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10710170

## Citation

> US National Institutes of Health, RePORTER application 10710170, Efficient synthon-based modular screening of Giga-to-Terra-scale virtual libraries (5R01GM147537-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10710170. Licensed CC0.

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