# Computational approaches to discover ligands with new chemotypes and functional properties

> **NIH NIH R35** · UNIVERSITY OF SOUTHERN CALIFORNIA · 2024 · $69,167

## Abstract

Project Summary
Discovery of selective and potent ligands for novel targets, as well as ligands with new functionalities remains a
slow and expensive process, hampering pharmacological validation of targets and discovery of new treatments
for many conditions like pain, substance abuse, Alzheimer's disease and cancer. Our research aims at
developing a scalable computational platform for ligand discovery, synergistically combining the advantages of
structure-based and data-driven approaches. We will pursue three synergistic technological directions,
combined with their experimental validation and application to clinically relevant targets. The first one builds
upon our recently introduced highly scalable synthon-based approach, V-SYNTHES, which performs hierarchical
structure-based virtual screening of giga- and tera-scale on-demand chemical spaces. We will further improve
the performance of the method by employing complementary Machine Learning approaches to ligand selection,
and optimize V-SYNTHES parameters to expand it to Tera-scale REAL libraries. The second research direction
combines V-SYNTHES screening with synergistic experimental hit identification approaches like fragment-
based, covalent screening and DNA-Encoded Library. Such hybrid methods build on complementary strengths
of these tools, enabling ligand discovery for the most challenging targets like cryptic and allosteric pockets.
Finally, building upon our extensive experience with the rational design of functionalized ligands, we will explore
structure-based approaches to designing photoswitchable, irreversible, bitopic and bivalent ligands, based on
both derivatives of known ligands and newly discovered chemotypes. Our broad network of experimental
collaborators will allow rapid synthesis of predicted compounds, and their comprehensive experimental validating
in biochemical, cellular and in vivo assays. Successful completion of this project will establish robust
computational and hybrid platforms for structure-based ligand discovery in most classes of therapeutic targets,
scaleable for rapidly growing REAL chemical spaces. The platform will be also thoroughly validated on a diverse
set of clinical targets, yielding new chemical probes and potential leads for drug discovery.

## Key facts

- **NIH application ID:** 10842818
- **Project number:** 1R35GM153437-01
- **Recipient organization:** UNIVERSITY OF SOUTHERN CALIFORNIA
- **Principal Investigator:** VSEVOLOD KATRITCH
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $69,167
- **Award type:** 1
- **Project period:** 2024-09-22 → 2029-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10842818

## Citation

> US National Institutes of Health, RePORTER application 10842818, Computational approaches to discover ligands with new chemotypes and functional properties (1R35GM153437-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10842818. Licensed CC0.

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