Drug development AI
you can fully audit.

Allen Institute collaboration ~170k arm-level facts in the graph FTO live to customers
The problem

AI for drug development has four
compounding failure modes

Each failure compounds the next. Stitched together, they produce predictions that look credible and aren't.

Priors bias predictions

16% of top-20 oncology targets flip when names are revealed.

Cuccarese (Recursion VP), 2026 · public disclosure

Elman→grounded in extracted features, not priors

Memorisation hides as reasoning

Up to 87% of medical pre-training content persists through fine-tuning.

Memorization in LLMs in Medicine, 2025.

Elman→claims trace to source arm + figure / table / sentence

Brute-force averages bury the signal

Phenotype-driving cells: <1% of a sample. Bulk -omics averages them out.

Van de Sande et al., Nature Reviews Drug Discovery, 2023.

Elman→resolves signal at cell, tissue, sub-population level

Depth doesn't scale

Reading thousands of papers at depth breaks on cost, time, and orchestration debt.

Elman→hundreds-to-thousands of papers and in-silico runs, at depth
Approach

Three properties.
One assembled intersection.

Each component is replicable. The intersection is what holds.

01 — Full provenance

Every edge resolves to a source

Every claim points to a paper figure, in-silico run, or experiment ID. The model never paraphrases from memory — it reads what it cites.

02 — Rational pruning

Edge-by-edge, not bulk-average

Reasoning agents chase specific questions through the graph — surfacing the low-magnitude signals at cell and sub-population scale where disease-driving biology sits.

03 — Compounding

One schema. Every domain.

A fact extracted in cancer is reusable in autoimmune, neuro, ageing. Graph grows ~88 arm-level facts per discovery cycle.

McKinsey · 2024 ~32% of pharma have scaled gen AI; ~5% report consistent differentiation.

Verifiable output at scale is what unblocks the 95%.

The product suite

Six products. One platform.

In production today

FTO

Live

Claim-level freedom-to-operate.

  • Per-component decomposition with a claim-level evidence trail on every verdict — not a similarity score on the whole filing.
  • Defensible under IP-committee review — one isolated agent per patent, no cross-contamination.

Verification

Live

Auditable claim validation.

  • Source-span resolution on every claim — paper sentence, trial arm, or figure.
  • Audit-grade depth across hundreds-to-thousands of papers and in-silico runs per claim, with pharma-specific provenance.

The rest of the suite

Hypothesis Generation

What testable hypotheses fall out of this dataset?

Patentability

What in here is novel and defendable?

Bioinformatics-at-scale

What does the raw cohort data reveal at single-cell resolution?

Effect Simulation

If I modify a target — add, inhibit, or combine — what happens downstream?

In active development
Applied use-cases

Nine use-cases.
Composed from the suite.

Available today

FTO assessment
↳ FTO
Patentability assessment
↳ Patentability
Verification audit
↳ Verification + Bioinformatics-at-scale
Target identification
↳ Hypothesis Generation + Verification + Bioinformatics-at-scale
Drug-drug interactions
↳ Hypothesis Generation + Verification + Bioinformatics-at-scale

Via scoped pilots

Biomarker discovery
↳ Hypothesis Generation + Verification + Effect Simulation + Bioinformatics-at-scale
Drug repurposing
↳ Hypothesis Generation + Verification + Effect Simulation + Bioinformatics-at-scale
Trial cohort selection
↳ Hypothesis Generation + Verification + Effect Simulation + Bioinformatics-at-scale
Combination design
↳ Effect Simulation + Bioinformatics-at-scale
The full picture

Drug development, end to end.

Five iterative steps from data to therapy. We push verifiable AI into every step of the way.

Step 1

Collect datasets

Step 2

Produce hypotheses

Step 3

Find targets

Step 4

Model effects

Step 5

Engineer mitigation

Wet-lab data
Traction

Partner. Platform. Product.

Three live proof points — already in customers' hands.

Partner · active
Allen
Institute
Joint research collaboration with the Allen Institute.
Platform · today
~170k
Arm-level experimental facts in the graph — reusable across cancer, autoimmune, neuro, ageing.
Product · live
Live
FTO in live IP-committee decisions — claim-level evidence per verdict.
Where we're headed

Full-stack discovery partnerships with pharma — taking a named indication end to end, and sharing in the therapeutics we create together.

Founders

Two senior technical co-founders.

Working at the intersection of biology, AI, and architecture.

Moustafa Khedr

Dr. Moustafa Khedr

Co-founder · biology + architecture
linkedin.com/in/khedrm
  • PhD — Crick / UCL: 3D iPSC skeletal-muscle constructs for Duchenne muscular dystrophy modelling and ASO drug testing.
  • Buy-side biological-DD on gene-therapy assets — mechanism-risk and animal-model interpretation.
Francesco Moramarco

Dr. Francesco Moramarco

Co-founder · AI / engineering + architecture
linkedin.com/in/fm2g11
  • PhD in medical-text generation with large language models — University of Aberdeen.
  • Research Scientist (NLP) — UK clinical-AI scale-up; live medical-note generation from inception to production deployment.
Together at Deep Science Ventures
Co-architects of Elman — the platform behind the Allen Institute collaboration and the FTO products in production today.
Get started

Let's build verifiable AI
for drug development — together.

© Elman · 2026 · A Deep Science Ventures spin-out