Exscientia
Recursion's AI drug discovery platform pairing 2M weekly wet-lab experiments with a 50+ PB proprietary dataset, now incorporating Exscientia.
If your discovery bottleneck is the volume and quality of experimental data, the Recursion OS loop is hard to replicate internally — 2M weekly experiments and 50+ PB are not a licensing line item. First patient dosing for REC-3565, REC-4881 in Phase 2 for FAP and the Genentech neuroscience target all show forward motion. But the pipeline is still mostly preclinical and nothing is approved. Go in expecting partnership economics and an oncology or CNS angle. If you mainly need cheaper in-silico chemistry on well-characterised biology, commercial platforms like Schrödinger or a CRO relationship will be a shorter path.
Last checked 3d ago · cite: rightaichoice.com/tools/exscientia
- Pharma and biotech teams whose bottleneck is generating proprietary experimental data
- Oncology programs needing hit-to-IND support backed by automated wet-lab validation
- Rare disease developers (FAP, hypophosphatasia) seeking end-to-end discovery support
- CNS and neuroscience programs following the Genentech collaboration
- Buyers who need approved drugs or validated late-stage assets today — the pipeline is mostly preclinical
- Small biotechs without the budget or data infrastructure for a long platform partnership
- Teams that mainly need faster chemistry on well-characterized biology rather than new experimental data
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Skip Recursion if you need a per-seat tool you can trial this week, or if your biology is already well characterized and you just want cheaper in-silico chemistry — this is a multi-year partnership built around generating new experimental data.
Partnership deals typically carry multi-year commitments and milestone or royalty structures — the real cost of a program is rarely the upfront number quoted at signing.
There is no published tier list; the commercial shape is a co-development partnership, so your cost lands closer to a pharma R&D collaboration than to a SaaS line item. Budget at the scale of a multi-year discovery alliance, not a seat licence. Compare against other partnership-model AI discovery groups — Insilico Medicine and BenevolentAI — and against CRO-plus-software stacks (Schrödinger plus a chemistry CRO), which trade the data moat for lower and more predictable spend.
In short
Exscientia — Recursion's AI drug discovery platform pairing 2M weekly wet-lab experiments with a 50+ PB proprietary dataset, now incorporating Exscientia. Best for Pharma and biotech teams whose bottleneck is generating proprietary experimental data, Oncology programs needing hit-to-IND support backed by automated wet-lab validation, Rare disease developers (FAP, hypophosphatasia) seeking end-to-end discovery support. Contact Sales pricing.
No head-to-head comparison for Exscientia yet — the alternatives below are the closest matches we list.
Viability Score
How well maintained and how widely used is Exscientia? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Automated wet lab robotics running up to 2 million experiments per week
- 50+ petabyte proprietary dataset spanning phenomics and transcriptomics
- Phenomics cell imaging to map disease-driving cellular disruptions
- Proteomics and ADME data integration for compound profiling
- De-identified patient data integration for translational insight
- Machine learning for novel target identification
- Generative de novo chemistry for molecule design
- Wet-and-dry lab-in-the-loop closed feedback system
- Multi-omic data integration across biology and chemistry
- Synthetic accessibility filtering to cut compounds before lab synthesis
- Translational clinical tools de-risking hit ID through IND-enabling studies
- Pharmaceutical co-development partnerships with industry leaders
- Technology partnerships with computational titans
- Next-generation data partnerships supporting generative AI for pharma
- Pipeline spanning preclinical to pivotal/Phase 3 in oncology and rare disease
About Exscientia
Exscientia now operates as part of Recursion, and the combined company's AI drug discovery engine is the Recursion OS. The thesis has not moved in a decade: image cells, train models on those images, and map the cellular disruptions behind disease to attack the roughly 90% failure rate of conventional discovery. The difference is throughput. Automated labs run up to 2 million experiments per week, feeding a proprietary dataset of 50+ petabytes that spans phenomics, transcriptomics, proteomics, ADME and de-identified patient data. The loop is one connected system: robotics produces experimental results, machine learning converts them into target hypotheses and designed molecules, and the next wet-lab round validates. Recursion calls this a wet-and-dry "lab-in-the-loop" staffed by bilingual teams of scientists and technologists. The company claims a concrete efficiency marker — advancing novel candidates after synthesizing only around 300 compounds, roughly 90% fewer than the industry average, with molecules filtered for synthetic accessibility rather than in-silico promise alone. The pipeline is concentrated in oncology (solid tumors and lymphoma, advanced solid tumors, B-cell malignancies) plus rare disease (familial adenomatous polyposis, hypophosphatasia), spanning preclinical through pivotal/Phase 3. Current readouts include first patient dosing in the Phase 1 study of REC-3565, a selective MALT1 inhibitor for relapsed or refractory B-cell lymphomas, REC-4881 (a MEK1/2 inhibitor) in Phase 2 for FAP, and the Genentech collaboration reaching its first neuroscience target, extending the platform into CNS. The fit is pharma and biotech groups whose bottleneck is experimental data generation, not modelling.
Behind the Verdict
Exscientia's absorption into Recursion makes this page a Recursion evaluation. What you get is an end-to-end drug discovery system rather than software you install: automated labs running up to 2 million experiments weekly, a 50+ petabyte proprietary dataset spanning phenomics, transcriptomics, proteomics and ADME, machine learning for target identification, generative de novo chemistry, and translational clinical tools carrying candidates from hit identification to IND-enabling studies. Strengths. The data moat is the whole argument. Cell imaging at that scale produces training data competitors cannot buy, and the company ties the loop together — wet-lab results feed models, models propose targets and molecules, the next experiment validates. The stated efficiency marker is unusually concrete: quality novel candidates advanced after synthesising only around 300 compounds, roughly 90% fewer than industry average, with synthetic accessibility filtering applied before anything reaches the bench. That last detail matters because it distinguishes designed molecules that can actually be made from in-silico molecules that cannot. Weaknesses. Speed of commercial access is the big one: this is a partnership model, so procurement runs through business development rather than a procurement portal, and the multi-year co-development commitments typical here are not a short pilot. The pipeline, while advancing, is early — nothing is approved, and REC-3565 only just dosed its first patient in the Phase 1 study, so readouts from the AI-designed molecules are still pending. Reliance on proprietary data also limits external reproducibility, which is a fair question if your scientists want to audit the models themselves. Where it fits. Oncology programs needing hit-to-IND support backed by automated wet-lab validation; rare disease developers in FAP or hypophosphatasia; CNS programs riding the Genentech collaboration's first neuroscience target; pharma groups with a data-generation bottleneck. Where it doesn't. Teams with well-characterised biology who mainly want faster chemistry — a CRO or a chemistry-focused platform is cheaper. Small biotechs without the budget for a long platform partnership. Anyone shopping on per-seat pricing.
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Real-world workflow fit
Concrete scenarios for the personas Exscientia actually fits — and what changes day-one when you adopt it.
You have a validated solid-tumor target but no proprietary phenotypic data. You partner for hit identification; Recursion runs cell-imaging phenomics screens in its automated labs and returns target hypotheses plus designed molecules filtered for synthetic accessibility.
Outcome: You reach a shortlist of makeable candidates having synthesized roughly 300 compounds rather than thousands, moving into IND-enabling studies faster.
Your FAP or hypophosphatasia program is stalled at preclinical. You co-develop, using multi-omic and de-identified patient data integration to map the disease biology, then generative de novo chemistry to propose molecules.
Outcome: A first-in-class candidate path with wet-lab validation built in, benchmarked against Recursion's own REC-4881 Phase 2 experience in FAP.
You want neuroscience targets outside your internal screening capacity. You join the Genentech-style collaboration structure to access the platform's target-unlock work in CNS.
Outcome: Access to a first neuroscience target unlock and a wet-and-dry loop you would otherwise have to build.
Use Cases
- Identify novel drug targets by analyzing multi-omics patient data and cellular imaging.
- Design optimized small molecules using generative AI models trained on proprietary compound data.
- Accelerate preclinical development from hit identification to IND-enabling studies using automated wet-lab feedback.
- Develop precision oncology therapies for solid tumors and lymphomas through AI-driven biomarker discovery.
- Partner with pharma companies to co-develop drugs in oncology, rare disease and CNS.
Models Under the Hood
as of 2026-10-16
Limitations
- Access runs through a business-development partnership rather than a self-service tier, so procurement is a multi-stakeholder process and the platform is built for enterprise partnerships, not individual researchers.
- Reliance on proprietary data limits external transparency — you cannot independently audit the 50+ PB dataset.
- The pipeline is early-stage with no approved drugs yet; REC-3565 only just dosed its first patient in Phase 1, so clinical validation of the AI-designed molecules from that program is still pending.
- Fit is narrow: oncology, rare disease and (via Genentech) CNS.
as of 2026-10-18
Verification history
We have re-verified Exscientia 22 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 22 verification passes.
Free to cite with attribution — this page re-verifies continuously.
Where the pricing makes sense
The company stage and team size where Exscientia's pricing actually pencils out — and where peers do it cheaper.
There is no published tier list; the commercial shape is a co-development partnership, so your cost lands closer to a pharma R&D collaboration than to a SaaS line item. Budget at the scale of a multi-year discovery alliance, not a seat licence. Compare against other partnership-model AI discovery groups — Insilico Medicine and BenevolentAI — and against CRO-plus-software stacks (Schrödinger plus a chemistry CRO), which trade the data moat for lower and more predictable spend.
Setup time & first value
How long it actually takes to get something useful out of Exscientia — broken out by persona, not the marketing-page minute.
Partner personas should plan weeks to months, not days: scoping a therapeutic area, legal and IP terms, and data-sharing agreements come before any experiment runs. Expect the first experimental data weeks after the contract closes, and candidate-level output on a months-to-years horizon — REC-3565 reached first patient dosing in Phase 1 only after a long discovery and IND-enabling stretch.
Switching to or from Exscientia
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From an internal screening group: keep your target biology in-house and route compound design and wet-lab validation through the platform's lab-in-the-loop.
- →From a chemistry CRO: replace sequential design-make-test cycles with a closed loop where each wet-lab round retrains the design models.
- →From an in-silico-only discovery tool: inherit a wet-lab validation layer so designed molecules are tested before synthesis spend.
- ↗To a discovery CRO: you lose the proprietary dataset and retain only the molecules and data generated under your partnership terms.
- ↗To a chemistry-focused software stack: you keep your own compounds but give up automated phenomics screening throughput.
Resources & Guides
- Resourceexscientia.ai
Pioneering AI Drug Discovery | Recursion
Helpful link from exscientia.ai
- Resourceexscientia.ai
Pioneering AI Drug Discovery | Recursion
Helpful link from exscientia.ai
- Resourceexscientia.ai
Pioneering AI Drug Discovery | Recursion
Helpful link from exscientia.ai
- Resourceexscientia.ai
Pioneering AI Drug Discovery | Recursion
Helpful link from exscientia.ai
- Learnexscientia.ai
Pioneering AI Drug Discovery | Recursion
Educational content from exscientia.ai
- Quickstartexscientia.ai
Pioneering AI Drug Discovery | Recursion
Get up and running fast from exscientia.ai
- Guideexscientia.ai
Pioneering AI Drug Discovery | Recursion
In-depth how-to from exscientia.ai
- Tutorialexscientia.ai
Pioneering AI Drug Discovery | Recursion
Step-by-step walkthrough from exscientia.ai
- Documentationexscientia.ai
Pioneering AI Drug Discovery | Recursion
Full product docs from exscientia.ai
- Resourceexscientia.ai
Pioneering AI Drug Discovery | Recursion
Helpful link from exscientia.ai
Tutorials & Learning
YouTube returned 6 videos for “Exscientia”, and we withheld 6: 6 could not be judged, because “Exscientia” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Exscientia.
Official links
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