# AI-Accelerated Discovery of Novel Compounds for GPCRs Targeted by Drugs of Abuse

> **NIH NIH R21** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2024 · $253,500

## Abstract

PROJECT SUMMARY
The addiction and overdose crisis in the United States has reached a record high, with almost 109,000 overdose
deaths in 2021, the majority of which are due to opioid overdoses, according to the Centers for Disease Control
and Prevention. Medications exist for treating brain
Expanding the range of effective therapeutics for substance
disorders caused by drug abuse, but
 use disorders, particularly opioid use disorders
they have limitations.
(OUDs), is necessary and urgent.
T
he National Institute on Drug Abuse's Division of Therapeutics and Medical
Consequences has identified a number of G protein-coupled receptors (GPCRs) modulated by functionally
distinct ligands as key targets for developing novel therapeutics to treat opioid overdose and opioid use disorders.
Identifying specific and selective small-molecule ligands for these receptors is crucial for understanding their
function and developing effective treatments. However, this is a challenging task due to the functional complexity
of GPCRs and the difficulty in customizing ligands for them, despite advances in GPCR functional and structural
biology. With the increasing availability of functional data from sophisticated bioactivity assays, billion-scale
electronic chemical libraries, and ever-growing high-resolution structural information on GPCRs, it is important
to develop quantitative and analytical approaches to leverage knowledge and information towards the
development of effective medications for OUDs. The proposed research aims to develop Artificial Intelligence
(AI)-driven strategies to design customized GPCR ligands and efficiently screen ultra-large electronic chemical
libraries to speed up the discovery of novel chemical compounds with distinct pharmacological profiles targeting
GPCRs linked to drug abuse. Specifically, the study will investigate the performance of deep neural network
classifiers trained on large datasets of key structural and physicochemical properties of ligands and targets from
receptor subfamilies and compare it with the performance of classifiers trained on features from a single GPCR
of those subfamilies in distinguishing between ligands with varying efficacy at that receptor. The goal is to create
a scalable platform that can add value to current rational drug design approaches for GPCRs associated with
drugs of abuse by identifying lead compounds that can be developed into successful drugs for clinical
applications.

## Key facts

- **NIH application ID:** 10774485
- **Project number:** 1R21DA059420-01
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** Marta Filizola
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $253,500
- **Award type:** 1
- **Project period:** 2024-04-01 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10774485

## Citation

> US National Institutes of Health, RePORTER application 10774485, AI-Accelerated Discovery of Novel Compounds for GPCRs Targeted by Drugs of Abuse (1R21DA059420-01). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10774485. Licensed CC0.

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