# A computational platform to improve the prioritization of drugs and target genes for therapeutic intervention

> **NIH NIH R44** · GENETIC NETWORKS, LLC · 2020 · $974,572

## Abstract

Project Summary
This project proposes further development of a computational discovery platform shown to quantifiably
identify and prioritize both targets and compounds for therapeutic intervention, which will accelerate the
development of newer, more effective drugs with fewer side-effects. This platform is based on a novel
fusion of two state of the art computational approaches (phenolog mapping and functional networks) with
proprietary data from Genetic Networks powerful gene-drug screening assays (H-Tech and Y-Tech) that
identify drug targets, conserved drug target pathways, and off-target effects (e.g. toxicity) by genome-
wide phenotypic profiling. The computational platform identifies conserved and functionally linked genes
to define conserved biological modules, groups of gene that work tightly together and contribute to
disease phenotypes and drug responses.
The first aim is to improve the throughput and robustness of Genetic Networks computational platform to
provide the pharmaceutical industry with a proven tool to select better compounds for their drug
development pipelines and clinical trials. The second aim is to improve the prioritization of target genes
that will be most effective in treating disease. Many genes have very similar copies, known as paralogs,
which can complicate the interpretation of biological data. This project will integrate diverse biological
information to prioritize the best gene target based on the disease, the relevant tissue, and how each
gene interacts with other genes. The third aim is to identify common groups of genes involved in multiple
disease and/or multiple drug responses. Understanding the genes involved in multiple biological
processes allows pharmaceutical companies to repurpose already approved drug for new purposes and
lowers the cost of developing multiple drugs for multiple disease. In addition, this approach will identify
the potential drug interactions that can lead to severe side-effects when drugs are taken together without
requiring animal testing or risking patient lives.
This automated system will rapidly identify and prioritize therapeutic interventions across multiple
diseases and will increase the success rate of drug discovery and provide guidance to repurpose existing
drugs for new indications. Implementation of the platform described in this proposal will strengthen
Genetic Networks' contributions to the goal of bringing new treatments to patients faster.

## Key facts

- **NIH application ID:** 9979989
- **Project number:** 5R44TR002035-03
- **Recipient organization:** GENETIC NETWORKS, LLC
- **Principal Investigator:** Raymond Joseph Terryn
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $974,572
- **Award type:** 5
- **Project period:** 2019-07-17 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9979989

## Citation

> US National Institutes of Health, RePORTER application 9979989, A computational platform to improve the prioritization of drugs and target genes for therapeutic intervention (5R44TR002035-03). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/9979989. Licensed CC0.

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