# NIH grant: AI-Powered Computational Models for Human-Biology-Based Translational Research (1R35GM166095-01) — retained record

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NIH grant 1R35GM166095-01: AI-Powered Computational Models for Human-Biology-Based Translational Research
Institute: {'code': 'GM', 'abbreviation': 'NIGMS', 'name': 'National Institute of General Medical Sciences', 'admin_org_id': '12500024', 'admin_funding_url': ''}. PI: Anjun  Ma. Award: $383169.
Start: 2026-07-23T00:00:00.
SUMMARY
Animal models and organoid systems are indispensable for preclinical research, yet their translational accuracy
remains limited, with fewer than 10% of therapies effective in mice ultimately succeeding in humans due to
fundamental species-specific genetic, molecular, and cellular differences. Organoid systems offer an alternative,
but their cellular composition and regulatory programs remain immature relative to human tissues, and a central
challenge is the absence of standardized computational frameworks to benchmark the fidelity of these models.
At the same time, virtual cell technologies are emerging, but current implementations are fragmented, rely on
single data types, lack mechanistic grounding, and often produce inconsistent perturbation predictions, all of
which prevent reliable translation from preclinical studies to effective human therapies. This R35 program will
establish innovative AI-driven strategies to overcome these challenges through two integrated directions.
Direction 1 will develop a fidelity framework to rigorously benchmark organoid and animal models against human
references by applying variance decomposition and batch reasoning to disentangle biological signals from
technical noise, using heterogeneous graph transformers with contrastive learning to align conserved programs
while preserving species-specific divergence. The outcome will be fidelity scorecards that quantify human
relevance across regulatory programs, cell–cell communication, and tissue architecture, thereby highlighting
deficiencies and providing blueprints for organoid optimization. Direction 2 will construct ensemble virtual cells
to predict human-like cellular responses under perturbations by integrating existing gene-expression, protein,
and regulatory models into a universal representation across modalities and scales, using hypergraph
transformers to harmonize molecular, cellular, and multicellular features, and applying dynamic operator learning
to simulate perturbation trajectories with uncertainty while incorporating interactome and metabolome knowledge
to capture species-specific drug adaptations. Together, these directions create a closed loop where fidelity
benchmarks guide virtual cell grounding and virtual cells provide actionable translational predictions. Applications
include accurate synotype characterization in lung disease, where organoids must capture fibroblast
heterogeneity and senescence, and cancer drug target prioritization, where virtual cells identify conserved
druggable modules while flagging human-specific liabilities. By providing reproducible metrics, interpretable
predictions, and uncertainty-aware outputs, this program directly addresses reproducibility and translation in
preclinical science, building on my lab’s expertise in graph neural networks, heterogeneous graph transformers,
and drug response prediction. Ambitious yet feasible, supported by strong preliminary tools, collaborative
datasets, and validated workflows, the proposed research will redefine how preclinical data are evaluated,
benchmarked, and applied, ultimately accelerating organoid development, improving drug pipelines, and
reducing costly translational failures to improve human health.
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