The NIH’s groundbreaking shift to prioritize human-relevant science marks a paradigm change for biomedical research institutions. This toolkit outlines how VeriSIM Life’s BIOiSIM® platform enables researchers to align with NIH’s new direction, replacing traditional animal testing with predictive, clinically relevant AI-driven tools. For institutions, this shift doesn’t just improve science—it enhances funding competitiveness, reduces R&D costs, and accelerates translational impact.
Increased NIH Funding Eligibility: Align with priority areas for new NIH grant opportunities focused on human-based research.
Lower Preclinical Costs: Reduce or eliminate animal studies, which often represent 30–50% of early-stage R&D budgets.
Faster Time to IND: BIOiSIM® de-risks candidates earlier, cutting 6–12 months from development timelines.
Higher Translational Success Rates: Replace low-predictive animal models with human-relevant simulations, increasing clinical success probability.
Join a forward-looking ecosystem of AI-powered translational research and attract academic-industry collaborations.
AtlasGEN Novel Drug Designer efficiently identifies new molecules based on favorable chemical properties and simultaneously evaluates them for clinical safety and effectiveness based on biology using VeriSIM Life’s groundbreaking Translational Index™ technology.
Iterative ranking of target engaging hits by their Translational Index values reduces the time and cost associated with other computational hit discovery approaches, while compressing the hit-to-lead refinement and optimization process by pre-validating hits for translatability. This unique integration dramatically reduces the number of compounds to evaluate in subsequent experimental research.
The ability to generate target engaging compound hits depends largely on the size of the molecular search library. The AtlasGEN search space is supported by more than 1 trillion compounds. Generative AI and deep learning techniques deliver unprecedented discovery capability.
AtlasGEN accurately predicts protein-lygand binding affinity by combining geometric conformer analysis with machine learning based on experimental data 72% more efficiently than molecular docking-based approaches.
.png)
