May 17, 2021
PA-20-183 - NIH Research Project Grant (Parent R01 Clinical Trial Required)
PA-20-185- NIH Research Project Grant (Parent R01 Clinical Trial Not Allowed
PA-20-184 – NIH Research Project Grant (Parent R01 Basic Experimental Studies with Humans Required)
National Institute on Drug Abuse (NIDA)
The purpose of this Notice is to inform potential applicants to the National Institute on Drug Abuse (NIDA) of NIDA's interest ingrant applications that will develop or utilize advanced computational approaches to describe complex drug-disease relationships in ways that will rapidly advance the development of new treatments, allow for targeted funding of substance use disorder (SUD) drug discovery and improve health care.. NIDA has a particular interest in applications thatinclude approaches in one or more of the following categories: 1) artificial intelligence, including machine learning and deep learning, 2) supercomputing/parallel computing, and/or 3) quantum computing.
Addiction is a complex disease that includes many neuropathological, psychiatric, and environmental influences, and biomedical research in this area is generating unprecedented amounts of data, much of which is distributed and exists in a wide variety of formats. Utilizing and/or combining artificial intelligence (including machine learning and deep learning), supercomputing/parallel computing, and quantum computing approaches can help us better understand the various underlying mechanisms (genetic, cellular, molecular) of addiction. These approaches allow for models that mimic the human brain and address issues of scale, complexity, and processing speed of the data, as well as the integration of disparate data elements (such as gene expression data, multi-omics capture data, cellular signaling, receptor-drug interactions, pharmacological and behavioral responses, brain region activity, electronic medical records, etc.). Developing or utilizing advanced computational approaches can also help us address unmet needs in clinical and outcome studies, identifying translational biomarkers, and discovering new therapeutics and combinations of therapeutics that can more effectively treat SUDs. Collectively, these approaches will enable predictive disease modeling, advance the development of new treatments, and improve healthcare. Additionally, combining a variety of advanced computational approaches will allow researchers from multiple disciplines and backgrounds (industry and academia) to come together to model and study disease pathology addiction in novel ways.
NIDA is interested in combining the latest computational capabilities with biomedical data to enhance our understanding of biology of addiction and improve drug discovery and development. AI, including ML and DL, enables computers to mimic the human brain, while supercomputing/parallel computing addresses the scale and complexity of data, and quantum computing addresses processing speed. One or more of these approaches may be used to analyze gene expression data, multi-omics capture data, cellular signaling, receptor-drug interactions, pharmacological and behavioral responses, brain region activity, electronic medical records, or other data to address fundamental research questions associated with the prevention, diagnosis, and treatment of addiction.
Examples of approaches that are encouraged include, but are not limited to:
Areas of programmatic interest to NIDA include:
Application and Submission Information
This notice applies to due dates on or after October 5, 2021 and subsequent receipt dates through January 8, 2025.
Submit applications for this initiative using one of the following funding opportunity announcements (FOAs) or any reissues of these announcement through the expiration date of this notice.
All instructions in the SF424 (R&R) Application Guide and the funding opportunity announcement used for submission must be followed, with the following additions:
Applications nonresponsive to terms of this NOSI will not be considered for the NOSI initiative.