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Those distinctions describe operating models, not a proven ranking of drug companies. Isomorphic Labs has reported encouraging computational benchmarks and disclosed major pharmaceutical partnerships, but those facts do not establish clinical success or superiority across the full path from target selection to an approved medicine.
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The short answer: a unified design engine versus broader discovery platforms
Isomorphic Labs’ public positioning centers on IsoDDE, a proprietary, unified computational drug-design engine. The company says it is target- and disease-agnostic, can work across therapeutic areas and modalities, and is intended to predict more than a protein’s shape. Its February 10, 2026 technical report describes tasks including protein–ligand and protein–protein interactions, antibody–antigen interfaces, binding affinity and identification of novel binding pockets.
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That emphasis gives Isomorphic a different public identity from two useful comparators:
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- Recursion describes Recursion OS as an integrated system that joins physical wet-lab experimentation, proprietary data, computational analysis and machine-learning models.
- Insilico Medicine describes Pharma.AI as a commercially available, end-to-end generative-AI software and automation platform with tools across biology, chemistry, medicine and science.
In practical terms, Isomorphic foregrounds a single biomolecular-design engine and uses it in internal and partnered programs. Recursion foregrounds an experimental data-generation loop. Insilico foregrounds software access as well as collaborations and drug programs. The public materials do not provide an independent, like-for-like clinical comparison that identifies one overall winner.
What does Isomorphic Labs’ AI drug-design engine do?
From structure prediction to interaction and affinity questions
Traditional structure prediction answers an important but limited question: what three-dimensional arrangement might a biomolecule adopt? Isomorphic says IsoDDE is designed to address the next questions that matter in drug design: whether a candidate molecule will interact with a target, how that interaction is arranged, how strong the binding may be and whether a target contains a previously unrecognized pocket that a drug could exploit.
That is why the company describes IsoDDE as extending beyond AlphaFold 3 rather than simply repackaging a structure-prediction model. The stated goal is a unified computational representation that can support several related design decisions instead of handing separate steps to unrelated tools.
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A single engine across targets, diseases and modalities
Isomorphic’s technology materials describe the engine as working across multiple therapeutic areas and modalities. The company also says it has internal oncology and immunology programs alongside partnered research. “Target- and disease-agnostic” should be read as a description of intended scope: it does not mean every target or modality has equal data quality, predictive accuracy or development maturity.
Johnson & Johnson’s disclosed collaboration illustrates the breadth Isomorphic claims publicly. That agreement spans multiple targets and modalities, including small molecules and biologics. By contrast, the Novartis and Eli Lilly descriptions emphasize small-molecule therapeutics, with some targets kept undisclosed.
Why Isomorphic says AlphaFold alone is insufficient
Max Jaderberg, Isomorphic Labs’ president, described AlphaFold 3 as “a major leap forward in understanding biological structures,” while adding that “we know we’re never going to solve drug design with AlphaFold alone.” The distinction is consequential: a model that predicts a plausible structure is not automatically a system that can select a developable compound, estimate its affinity, anticipate selectivity or guide experiments through a medicinal-chemistry cycle.
IsoDDE’s stated value proposition is therefore integration. The company wants one system to connect structural biology with the interaction and design predictions that occur later in the discovery workflow. Whether that integration improves the probability of a clinical success remains an empirical question, not something established by the platform description itself.
What do Isomorphic Labs’ benchmark results show?
The FoldBench figures
In its February 2026 technical report, Isomorphic Labs reports FoldBench success rates for several interaction-prediction tasks and compares them with AlphaFold 3. The figures below are the values published by Isomorphic Labs under the report’s definitions and evaluation set.
| FoldBench task | IsoDDE | AlphaFold 3 |
|---|---|---|
| Antibody–antigen | 75.58 | 47.90 |
| Protein–ligand | 75.99 | 64.90 |
| Protein–protein | 74.19 | 72.93 |
The same report says IsoDDE achieved more than a twofold accuracy improvement over AlphaFold 3 in the hardest similarity category of the Runs N’ Poses protein–ligand benchmark. That is a task-specific result, not a claim that IsoDDE produces twice as many successful medicines.
How to interpret the numbers correctly
These are computational benchmark success rates. They tell you how the systems performed on defined evaluation tasks, with the report’s particular datasets, categories and scoring rules. They do not measure:
- the number of candidates that reach human trials;
- clinical safety, efficacy or regulatory approval;
- the cost or speed of a complete discovery campaign;
- performance on every target class, disease area or chemical series; or
- independent replication by an outside laboratory.
The report is valuable evidence that Isomorphic has built and tested a system on specified prediction problems. It is not independent proof of clinical utility, and it does not establish a universal advantage over every other AI drug-discovery approach.
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How is Isomorphic Labs different from Recursion?
Recursion’s stated operating loop
Recursion’s public FAQ describes Recursion OS as a combination of physical wet-lab experimentation, proprietary data, computational analysis and machine-learning models. The distinctive emphasis is the feedback loop: experiments generate large amounts of biological data, computational systems analyze those observations, and the resulting hypotheses guide further experiments.
Isomorphic’s public materials instead lead with a computational design engine and interdisciplinary drug-design teams. They do not quantify how much wet-lab work is performed internally or specify a single experimental operating system equivalent to Recursion OS. That difference is about where each company places its public center of gravity, not proof that one company performs no laboratory work.
What the difference means for a pharmaceutical partner
A partner evaluating Recursion may focus on the scale, automation and proprietary nature of its experimental data-generation loop. A partner evaluating Isomorphic may focus on whether IsoDDE can answer a sequence of structural and molecular-design questions for a difficult target and then support a chemistry or biologics program.
The two approaches can overlap. Recursion’s data and experiments can support model development, while Isomorphic’s computational predictions still require experimental confirmation. The available company descriptions do not establish that one workflow consistently delivers better candidates, faster timelines or lower costs.
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Platform access versus an engine used for programs
Insilico’s Pharma.AI page presents a commercially available generative-AI software and automation platform. Its stated coverage spans biology, chemistry, medicine and science, making software access and collaborations explicit parts of the offering.
Isomorphic’s public materials emphasize using IsoDDE to advance partnered and wholly owned therapeutic programs. The company has not presented the same public positioning as a general-purpose software product that customers can simply license across all discovery functions. This is a business-model distinction: commercial platform access is not the same as using an internal engine to generate company-owned or partner-owned assets.
Similar breadth, different evidence questions
Both companies describe activity across several discovery stages or modalities, but breadth alone does not establish equal maturity. For Insilico, the key diligence questions include which modules a customer receives, how automation connects to laboratory work and what validation exists for each stage. For Isomorphic, the questions include how IsoDDE performs on target classes outside the published benchmarks, how predictions are integrated into medicinal chemistry and biology experiments, and whether any program demonstrates clinical benefit.
What do Isomorphic Labs’ pharmaceutical partnerships reveal?
Novartis
Isomorphic says its collaboration with Novartis began in January 2024 and initially focused on small-molecule therapeutics against three challenging targets. The companies expanded the collaboration in February 2025. Novartis biomedical-research president Fiona Marshall said the work had exposed “new chemical spaces that would be unavailable to probe through traditional methods” and that she looked forward to work on additional difficult targets with the potential to address significant unmet patient needs.
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Eli Lilly
Isomorphic says its Lilly collaboration is seeking small-molecule therapeutics against undisclosed targets. Keeping targets undisclosed is common in competitive drug discovery, but it limits what outsiders can evaluate. The announcement demonstrates an active pharmaceutical relationship; it does not allow an independent assessment of the programs’ scientific progress.
Johnson & Johnson
The Johnson & Johnson collaboration covers multiple targets and modalities, including small molecules and biologics. This is the clearest public example of Isomorphic applying its stated engine beyond a single chemistry format. It still does not establish that every modality receives the same predictive performance or that a candidate will reach the clinic.
Isomorphic Labs’ internal programs and development path
Alongside partnerships, Isomorphic says it is pursuing wholly owned programs in oncology and immunology. That combination—partnered research plus internal assets—matters because it gives the company two routes for turning model outputs into drug candidates. Partnerships can provide disease expertise, target access, translational capabilities and development infrastructure; internal programs can let Isomorphic control program selection and retain a larger share of downstream value.
The public descriptions do not provide a complete pipeline dataset, phase-by-phase statistics or a list of every internal target. Readers should therefore treat “internal oncology and immunology programs” as a statement of activity, not as evidence that a particular candidate is clinically validated.
Funding is a scale signal, not an efficacy result
On May 12, 2026, Isomorphic Labs announced a $2.1 billion Series B led by Thrive Capital, with participation from Alphabet, GV, MGX, Temasek, CapitalG and the UK Sovereign AI Fund. The company said the proceeds would support further IsoDDE development and advance therapeutic programs toward the clinic.
A financing round of that size can fund compute, data, laboratory partnerships, hiring, translational studies and clinical preparation. It can also signal that major investors view the platform as strategically important. It cannot show that an AI-designed molecule is safe, effective or approvable. Capital committed to a platform and clinical programs remains an input to development, not an output from a successful trial.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical comparison of the three companies’ stated approaches
| Company | Primary technical emphasis | Wet-lab emphasis in public descriptions | Scope and modalities described | Commercial or development path |
|---|---|---|---|---|
| Isomorphic Labs | Unified computational engine for biomolecular prediction and drug design | Public materials emphasize in-silico design and interdisciplinary teams; the cited descriptions do not quantify internal wet-lab integration | Multiple disease areas, targets and modalities; disclosed J&J work includes small molecules and biologics | Strategic pharmaceutical collaborations plus internal oncology and immunology programs |
| Recursion | Recursion OS linking biological data, computation and machine learning | Wet-lab experimentation is explicitly part of the operating system | Platform and therapeutic-pipeline approach; the cited FAQ does not provide a modality-by-modality performance table | Platform-led discovery combined with a therapeutic pipeline |
| Insilico Medicine | Generative-AI software and automation across discovery functions | Commercial platform description spans biology, chemistry, medicine and science; the cited material does not provide a comparative wet-lab metric | End-to-end software coverage across several discovery stages | Commercial platform access and collaborations, alongside drug programs |
This table compares stated positioning. It should not be read as a league table: the sources do not establish equivalent datasets, development stages or independent clinical outcomes.
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What would count as stronger evidence?
Computational benchmarks are an early layer of evidence. A serious comparison of AI drug-discovery companies should add several more layers:
- Independent replication: outside groups reproduce results on preregistered or otherwise transparent datasets.
- Prospective validation: the system makes predictions before experiments, rather than explaining results after the fact.
- Experimental hit quality: predicted compounds show reproducible binding, selectivity, potency, activity in relevant cellular models and acceptable developability.
- Program progression: candidates move through lead optimization, toxicology and clinical-enabling studies with documented attrition.
- Clinical outcomes: controlled human studies demonstrate safety and efficacy for a defined indication.
- Operational measures: partners disclose time, cost, experiment counts and decision quality compared with a conventional workflow.
Isomorphic’s technical report addresses only part of the first stage, through company-reported computational evaluations. The partnership and funding announcements show activity and resources. They do not substitute for the later evidence layers.
Questions to ask when evaluating any AI drug-discovery claim
Is the result a model benchmark or a medicine?
Ask what was measured. A success rate on a protein–ligand benchmark is a model result. A response rate in a randomized clinical trial is a patient outcome. Conflating the two is the fastest way to overstate what an AI platform has achieved.
Who produced the evidence?
Isomorphic’s benchmark numbers come from its own technical report, and the platform and partnership descriptions come from company pages. Those are relevant primary statements, but they are not independent audits. The same standard applies to Recursion’s and Insilico’s descriptions of their platforms.
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Targets, compounds, negative results, experimental protocols and attrition rates may remain confidential for commercial reasons. A comparison should mark those boundaries rather than infer success from silence.
Does the platform cover the whole development chain?
Drug discovery is not one prediction task. Target biology, molecular design, synthesis, assay development, pharmacology, toxicology, manufacturing and clinical development each create different failure modes. A company may be excellent at one stage without controlling the others.
What sets Isomorphic Labs apart, in plain terms
- One integrated computational story: IsoDDE is presented as a unified engine rather than a collection of unrelated point tools.
- Predictions relevant to design: the stated scope includes interactions, affinity and binding pockets, not only static structure prediction.
- Cross-modality ambition: the company says the engine spans disease areas and modalities, with J&J publicly including small molecules and biologics.
- Dual development model: Isomorphic combines major pharmaceutical collaborations with internal oncology and immunology programs.
- Quantified but company-reported benchmarks: the February 2026 report supplies concrete comparisons with AlphaFold 3 on named tasks.
Those features make Isomorphic distinctive in public positioning. They do not prove that IsoDDE will produce more approved medicines than Recursion’s experimental-data loop or Insilico’s commercial generative-AI platform.
Bottom line
Isomorphic Labs stands apart because it is building and deploying a unified computational drug-design engine, IsoDDE, with ambitions that extend from molecular structure to interactions, affinity and binding-pocket discovery across diseases and modalities. Recursion offers the clearest contrast in operating emphasis, with wet-lab experimentation and proprietary data at the center of its stated system. Insilico offers the clearest contrast in commercial model, presenting an end-to-end generative-AI platform for software access and collaborations.
The fairest current conclusion is therefore descriptive, not winner-take-all: Isomorphic has a distinctive platform thesis, reported benchmark advantages on selected tasks, substantial pharmaceutical relationships and significant financing. Whether those advantages translate into safer, more effective medicines remains to be demonstrated through independent experiments, development milestones and clinical results.
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