# Towards a generalizable drug discovery framework based on intrinsically disordered regions

> **NIH NIH R01** · BRIGHAM AND WOMEN'S HOSPITAL · 2020 · $483,250

## Abstract

Project Summary
Current approaches to drug discovery are yielding diminishing returns as costs, failure
rate, and drug resistance all increase. Meanwhile, novel targets and drug candidates are
not keeping up with demand across the disease spectrum. This work seeks to address
several of these areas. It seeks to lower cost, increase success rate, and address drug
resistance while increasing novel targets and potential drug candidates.
While most drugs are found by trial-and-error or designed for specific structured protein
pockets, it turns out that many diseases and drug resistance occur at interfaces involving
disordered protein regions. So, while most informatics for drug design has focused on
structured protein pockets, an area with tremendous potential lies in disordered proteins
and their interfaces. To do so effectively, and at a large-scale, an informatics framework
is needed that effectively uses information across genomic, proteomic, structural,
chemical, pathway, ontological, interaction modeling, and evolutionary space.
Here, we present such a generalized, informatics framework that creates: 1) disordered
target libraries and corresponding small molecules to interact them and 2) small
molecules that can mimic disordered regions and thus interact with the usual partners of
the disordered protein regions. We will first create a disordered target library across
several organisms. Then, through a Bayesian framework, we will integrate expert
knowledge, sequence information/statistics, and interaction modeling to predict drugs
that can: 1) target these regions and 2) mimic these regions in interactions. Finally, we
will focus on drug resistant pathogens to validate predicted drugs experimentally.

## Key facts

- **NIH application ID:** 10000107
- **Project number:** 5R01GM118467-06
- **Recipient organization:** BRIGHAM AND WOMEN'S HOSPITAL
- **Principal Investigator:** GIL ALTEROVITZ
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $483,250
- **Award type:** 5
- **Project period:** 2016-09-01 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10000107

## Citation

> US National Institutes of Health, RePORTER application 10000107, Towards a generalizable drug discovery framework based on intrinsically disordered regions (5R01GM118467-06). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10000107. Licensed CC0.

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