What is a full-stack AI platform for drug development?
How real-world data (RWD), AI models, and compute work together as a full-stack platform to help increase a program’s likelihood of success.
Key Takeaways
- A full-stack AI platform unites three things in one place: proprietary RWD at scale, AI models and agents, and co-located compute — so teams can ask bigger questions, ask them more often, and trust the answers.
- The hardest questions in drug development live in data that public, internet-trained models were never shown, which is why the best model is only as good as the data it can reason over.
- At the scale precision medicine now operates, pharma needs to bring the analysis to the data, not the data to the model.
- Oncology’s probability of success from Phase 1 to approval sits near ~5–10%; a full-stack approach is how teams work to move that number — worth hundreds of millions per point.
Most enterprise AI in the life sciences began the same way: plug proprietary data into a public frontier model API and hope for results. That was a useful experiment, but it hit a ceiling. A full-stack AI platform is the response — an integrated system where data, models, and compute are engineered to work together rather than bolted onto one another. The articles below break down each layer of that stack and how it applies to drug development.
A CEO’s case for a full-stack approach to drug development
In Raising the Odds: How a Full-Stack Platform Unlocks Bigger, Better Opportunities, Tempus’ Ryan Fukushima, Data & Apps CEO, argues that impactful AI in drug development isn’t a neat demo — it’s shifting a critical decision you can defend to your team and to regulators. Getting there reliably means treating AI as a full-stack problem: uniting RWD at scale, models and agents tuned for the work, and compute that lives next to the data. As he puts it, “the advantage isn’t owning one ‘best’ model; it’s routing each task to whatever delivers the best answer, and being able to re-decide as the field evolves.” The payoff compounds: oncology’s Phase 1-to-approval odds sit near 10%, and pushing toward 50% becomes conceivable when the right data, people, and AI iterate together.
Why data comes first: the case for multimodal RWD
A model can only reason over what it has seen, and roughly 80% of clinical data lives in unstructured formats — notes, pathology reports, scanned molecular PDFs. The Multimodal Imperative explains that fragmented, single-modality data produces fragmented insights — and that integrating EHR, claims, molecular, and imaging data into a longitudinal, patient-level view is now a scientific necessity. Linking clinical and genomic data surfaces “hidden responders” that traditional trial criteria miss, and combining EHR with claims data supports synthetic control arms that reduce trial time and cost.
Learn why multimodal data is the foundation.
Why compute has to come to the data
Once a dataset is large enough, the bottleneck stops being data and becomes the compute needed to extract meaning from it. Computational Power Limits Oncology Innovation explains that when 550+ petabytes of data is paired with state-of-the-art NVIDIA GPU/CPU architecture, the physics flip: drug developers can bring the analysis to the data rather than shipping the data to the model. Traditional ETL pipelines and predictive APIs break down at this scale because foundation models need constant access to the entire dataset for training. Co-locating compute with the data is what makes digital twins, in silico trial simulations, and foundation model training possible in the first place.
Read why data gravity changes the architecture.
The model layer: foundation models as a “pre-trained biological engine”
Frontier models excel at reasoning and language, but they can’t infer biology they were never shown. In Q&A: How Foundation Models Are Accelerating Drug Development, Tempus AI leaders describe multimodal foundation models, trained on real-world patient data, as a "pre-trained biological engine." As Arpita Saha, VP of Applied AI and Research, puts it: "We should not be relearning the biology every time from scratch… but start from a trained understanding of biology."
Where it pays off: raising probability of technical and regulatory success (PTRS)
Oncology has the lowest success rate of any major therapeutic area — a candidate entering Phase 1 has roughly a 5% likelihood of approval. PTRS Is Built, Decision by Decision lays out the nine trial-design decisions, from biological rationale and biomarker stratification to control-arm benchmarking and endpoint strategy, that compound to determine that number, and how RWD and foundation models inform each one. Drawing on 45M+ de-identified patient journeys, including 400K+ with full multimodal data, and a patient-trajectory foundation model that reached a 0.802 C-index for overall survival in an EGFR-mutant NSCLC cohort, the piece shows how to pressure-test design assumptions before a protocol is locked. RWD doesn’t replace randomized evidence — it sharpens the decisions that surround the trial.
See how the full stack moves PTRS.
Book a demo to see what Tempus Lens can do for your programs.
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