New preprint: overcoming the accuracy-generalization tradeoff in docking and scoring for prospective virtual screening. Read the paper

Validated assets

We do not just run the models.
We run discovery programs.

These are not drugs we are developing in-house. They are vetted assets we have advanced through our discovery platform across oncology, cardiovascular, inflammation, and metabolic disease — available for out-licensing and co-development with partners ready to take them forward.

Available assets

Six assets across four therapeutic areas — available for partnership.

Each is a vetted target or hit series advanced through our discovery platform. Partners take them from here. Spotlights below carry the detailed content.

Engagement model

Out-licensing or co-development — not internal clinical development. We continue work on assets where partners want us to (or add new programs at partner request); the goal is dealable chemistry, not a drug-development pipeline of our own.

Target / Asset Therapeutic area Discovery stage Partnering status
GPR75 Lead asset Obesity / Metabolic Virtual Screening Lead asset, available for out-licensing
PCSK9 Cardiovascular Virtual Screening Actively screening — available for discussion
CD73 / 5NTE Oncology Hit-to-Lead Case study ready — available
IRAK4 Inflammation Hit Expansion Available for discussion
IL17A Inflammation Hit Expansion Available for discussion
Factor XI Cardiovascular Hit Expansion Available for discussion
Lead asset spotlight

GPR75 — an orphan GPCR with human genetic validation for obesity.

The target

GPR75 is an orphan GPCR validated by human genetics as a negative regulator of body weight. Loss-of-function variants associate with significantly lower BMI in large population studies, with no adverse effects in carriers — among the strongest human-genetic obesity signals published in the last decade.

No approved drugs. Open IP. Virtual screening underway.

What this opens up

  • Human-genetic validation — population-scale loss-of-function evidence rather than animal or phenotypic surrogates.
  • Open IP landscape — no approved drugs at this target; first-in-class window without competing programs to navigate around.
  • A new mechanism beyond GLP-1 — complementary to or differentiated from incretin-based programs depending on combination strategy.
  • Tractable on a novel target — limited structural precedent for an orphan receptor would normally constrain foundation-model dockers; our platform retains predictivity at 0–20% Tanimoto similarity (see Why Deep Origin).
Case study

CD73 — a 30× improvement over the industry hit-rate benchmark.

CD73 (5′-nucleotidase NT5E) — cell-surface enzyme converting AMP to adenosine; modulates immune suppression and tumor metabolism. Widely expressed in cancers.

Challenge

Industry screens against CD73 yield hit rates of 1% or less1,2 from hundreds to thousands of compounds synthesized. High-throughput screening is the standard approach — expensive, slow, and generates a lot of chemistry that goes nowhere.

What we did

Applied physics-based docking and multi-parameter computational filtering to a virtual compound library before any synthesis was commissioned.

Result
  • 80B compounds screened — ~80 billion synthetically accessible compounds screened; 183 experimentally tested.
  • 56 biochemical hits — including 54 <100 µM and 9 <10 µM IC₅₀.
  • 14 cellularly active compounds — including a 570 nM lead.
  • ~30% hit rate at <100 µM — substantially exceeding prior virtual-screening results.
  • Chemically novel non-nucleotide scaffolds — with low similarity to known CD73 inhibitors (ECFP4 Tanimoto mean ~0.22–0.27).
  • Hit-to-lead underway — using analogue-by-catalogue and cheminformatics-guided direct-to-biology strategies to accelerate DMTA cycles and lead optimization.
30× improvement in hit rate vs. the industry benchmark — translating directly into synthesis cost savings, faster lead identification, and a smaller, better-characterized compound set entering lead optimization.
Tanimoto similarity — identified hits cluster at ≈0.2, outside the training distribution. Compounds advanced are chemically novel.
Ranking of foundational models across 22 TDC tasks — Deep Origin Togo leads on early-ADME tasks; CYP-mediated inhibition remains a tradeoff. Request access for more detailed information →
  1. The Atomwise AIMS Program. AI is a viable alternative to high throughput screening: a 318-target study. Sci Rep 14, 7526 (2024). doi:10.1038/s41598-024-54655-z
  2. Kumar M, Lowery R, Kumar V. High-Throughput Screening Assays for Cancer Immunotherapy Targets: Ectonucleotidases CD39 and CD73. SLAS Discov. 2020 Mar;25(3):320-326. doi:10.1177/2472555219893632.

Open to a deeper conversation?

Detailed data sets, IP positions, and partnership structures are shared under CDA on request. Initial calls are with our BD lead.