Learn how to meet NIH’s latest grant requirement by incorporating AI-driven NAM strategies into your proposal - and how AccuraScience can help.
For many years, animal models have been a mainstay in biomedical research, especially for drug development and toxicity studies. Yet, experienced researchers understand well that animal experiments often fail to fully replicate human biology because of important species differences in metabolism, immune responses, and gene regulation. Besides, ethical concerns increasingly push the field to seek alternative approaches that reduce or replace animal use but maintain scientific rigor.
In response, NIH recently made a major policy shift: all new grant proposals must now include New Approach Methodologies (NAMs) - such as artificial intelligence, machine learning, and advanced computational modeling - to project how interventions will affect human health. This requirement is central to NIH’s effort to prioritize human biology–relevant research and reduce dependence on animal testing.
Computational models that lack mechanistic insight won't satisfy NIH reviewers. We help you build biologically grounded, reviewer-ready NAM strategies. Request a free consultation →
NIH reviewers will expect a strong NAM component in proposals, not as an option but a necessity. Proposals relying solely on animal data or lacking mechanistic computational approaches risk receiving lower scores or rejection. Researchers now need to:
- Integrate advanced computational modeling that goes beyond data analysis to provide mechanistic insight and predictive capacity.
- Demonstrate that models have biological plausibility and reproducibility consistent with human systems.
- Leverage complex human data sets-such as multi-omics or clinical data-to inform modeling strategies.
- Provide clear validation approaches tied to experimental or clinical evidence to convince reviewers.
Meeting these demands requires not only modeling and AI expertise but also the skill to translate complex computational results into clear, compelling scientific narratives.
| Research Domain | What You Need to Do | How AccuraScience Supports You |
|---|---|---|
| Drug Development & Toxicology | Build AI-driven mechanistic models predicting drug safety and efficacy using human-relevant data sources. | Develop, validate, and document predictive computational models; prepare clear grant computational plans. |
| Cancer & Disease Modeling | Use multi-omics and spatial data to computationally model tumor heterogeneity and therapy response. | Design integrated modeling pipelines; generate biologically interpretable predictive models for proposals. |
| Personalized Medicine | Create patient-specific “digital twins” combining genetic and clinical data to simulate treatment outcomes. | Build mechanistic digital twin models with validation and explanatory outputs suited for grant use. |
| Organ-on-a-Chip & Microphysiology | Apply AI and computational modeling for automated, quantitative analysis of experimental imaging data. | Provide customized AI modeling pipelines for image data analysis with quantitative result summaries. |
| General Biomedical Research | Incorporate NAM and computational modeling strategies with clear assumptions and validation plans. | Offer consulting to design NAM frameworks and translate complex modeling into accessible grant narratives. |
At AccuraScience, our practical experience implementing a wide range of published AI methods in biomedical research informs our NAM consulting services. You can read more about these concrete AI use cases in our detailed article, “10 Real-World AI Applications in Biology and Biomedicine You’ve Read About - But Might Need Help Implementing”. This resource demonstrates how we help researchers turn promising AI methods from publications into robust, interpretable models ready for grant proposals and publications.
Your AI pipeline must do more than predict - it must explain. We help you develop interpretable models that NIH reviewers trust. Request a free consultation →
Founded in 2013, AccuraScience was the first U.S. company to provide broad-spectrum, customized computational modeling and bioinformatics solutions tailored for academic researchers. Our Lead Bioinformaticians predominantly have 20+ years’ experience specializing in computational modeling, bioinformatics and AI research, with advanced degrees in both biology/biomedicine and computer science. Many have faculty experience at major U.S. institutions, securing multi-million dollar NIH grants and publishing over 40 peer-reviewed scientific articles.
Our deep computational modeling expertise combined with scientific insight uniquely equips us to help you design, implement, and document AI-driven NAM strategies that align with NIH’s new requirements and enhance your grant competitiveness.
The NIH’s NAM policy fundamentally changes how biomedical research funding decisions are made. To remain competitive, PIs must adopt rigorous AI and computational modeling approaches that are biologically sound, reproducible, and clearly communicated.
Send us an inquiry and take the first step toward adapting and succeeding in this new funding environment.
Read other articles in our blog series.
This blog article was co-authored by Zack Tu, Ph.D., Lead Bioinformatician and Justin Li, Ph.D., Lead Bioinformatician. To learn more about AccuraScience's Lead Bioinformaticians, visit https://www.accurascience.com/our_team.html.