Key Dates

Related Announcements

• May 7, 2020 - NIH Exploratory/Developmental Research Grant Program (Parent R21 Clinical Trial Not Allowed). See NOFO PA-20-195
• Mat 7, 2020 - NIH Exploratory/Developmental Research Grant Program (Parent R21 Clinical Trial Required). See NOFO PA-20-194
• May 7, 2020 - NIH Exploratory/Developmental Research Grant Program (Parent R21 Basic Experimental Studies with Humans Required). See NOFO PA-20-196
• May 5, 2020 - NIH Research Project Grant (Parent R01 Clinical Trial Not Allowed). See NOFO PA-20-185
• May 5, 2020 - Research Project Grant (Parent R01 Basic Experimental Studies with Humans Required). See NOFO PA-20-184
• May 5, 2020 -Research Project Grant (Parent R01 Clinical Trial Required). See NOFO PA-20-183
• May 5, 2020 - NIH Small Research Grant Program (Parent R03 Clinical Trial Not Allowed). See NOFO PA-20-200

Issued by

National Institute on Drug Abuse (NIDA)

Purpose

The proliferation of large-scale databases and biomedical repositories, accelerated by the transition to electronic health records and calls for increased data sharing, marks a significant shift in biomedical research. The new NIH policy on Data Management and Sharing will further amplify this trend, increasing the quality and accessibility of high-value datasets available for reuse and secondary analysis. Concomitantly, advances in artificial intelligence/machine learning algorithms (AI/ML) enable integration and analysis of vast, unstructured multimodal datasets. Computational modeling of complex relationships between small molecules, genes, proteins, neural circuits, and behaviors can be used to advance any stage of the medication development pipeline, including identification of medications suitable for repurposing, design and optimization of new chemical identities, design of regiments and prediction of side effects, to name a few. This data-driven technology has become ripe for full adoption in the development of novel treatments for substance use disorders (SUDs). For example, data from a genes-disease database, a genome encyclopedia, and a database of protein - chemical interactions have been integrated by a knowledge-driven AI-based system with more than 90 million electronic health records to identify ketamine, an FDA-approved medication, as a potential treatment for cocaine use disorder (PMID: 36792381).

The purpose of this Notice of Special Interest (NOSI) is to elicit projects that would leverage the power of generative AI/ML and predictive models to accelerate medication discovery and development for treatment of SUDs while reducing the risk of failure.

Areas of Interest:

• De novo design of new synthesizable molecules targeting addiction circuitry using generative AI tools, molecular dynamics simulations, and other advanced computational approaches 
• Identification of medications that can be repurposed for treatment of SUDs by constructing models based on multiple datasets such as chemical, genetic, and disease databases, electronic health records, clinical trials and scientific literature
• Optimization of promising compounds for SUD treatment to enhance their selectivity, affinity, and efficacy at the target receptor using structural evolution or other AI/ML approaches     
• AI-enabled virtual screening of medication candidates for those with high bioavailability, optimal pharmacokinetics, efficient blood brain barrier permeability, minimal toxicity, and desired behavioral responses
• Chemical synthesis design for the most effective synthetic route and ease of manufacturing by application of generative AI trained on databases of chemical reactions and structures

Other potential areas of interest:

• Intelligent neuromodulation protocols guided by AI/ML methods to minimize drug craving, improve impulse control, enhance executive function, reduce dysphoria, etc., for long-term recovery
• Combinatorial approaches designed through computational modeling and directed at multiple treatment targets simultaneously, combining different treatment modalities or different medications with synergistic effects on sustained drug abstinence
• Implementation of AI/ML algorithms to augment existing digital and sensor-based technologies monitoring stress, craving, drug relapse, or overdose
• AI/ML-based software as a medical device (SaMD) to support recovery, prevent relapse and improve treatment outcomes

Application and Submission Information

This notice applies to due dates on or after June 5, 2025 and subsequent receipt dates through September 8, 2028. 

Submit applications for this initiative using one of the following notice of funding opportunity (NOFO) or any reissues of these announcement through the expiration date of this notice.

• PA-20-185 - NIH Research Project Grant (Parent R01 Clinical Trial Not Allowed)
• PA-20-184 - Research Project Grant (Parent R01 Basic Experimental Studies with Humans Required)
• PA-20-183 - Research Project Grant (Parent R01 Clinical Trial Required)
• PA-20-195 - NIH Exploratory/Developmental Research Grant Program (Parent R21 Clinical Trial Not Allowed)
• PA-20-194 - NIH Exploratory/Developmental Research Grant Program (Parent R21 Clinical Trial Required)
• PA-20-196 - NIH Exploratory/Developmental Research Grant Program (Parent R21 Basic Experimental Studies with Humans Required)
• PA-20-200 - NIH Small Research Grant Program (Parent R03 Clinical Trial Not Allowed) 

All instructions in the How to Apply - Application Guide and the NOFO used for submission must be followed, with the following additions:

• For funding consideration, applicants must include “NOT-DA-26-005” (without quotation marks) in the Agency Routing Identifier field (box 4B) of the SF424 R&R form. Applications without this information in box 4B will not be considered for this initiative.

Applications nonresponsive to terms of this NOSI will not be considered for the NOSI initiative.

Inquiries

Please direct all inquiries to the Scientific/Research, Peer Review, and Financial/Grants Management contacts in Section VII of the listed notice of funding opportunity.

Scientific/Research Contact(s)

Jana Drgonova, Ph.D.
NIDA/DTMC
Telephone: 301-827-5933
Email: [email protected]