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Isomorphic Labs stands out primarily because it presents IsoDDE as one unified computational drug-design engine, rather than as a single prediction model or a collection of commercial software modules. The company says IsoDDE is designed to predict biomolecular interactions, binding affinity and previously unrecognized binding pockets across therapeutic areas, targets and modalities. Its public collaborations with Novartis, Eli Lilly and Johnson & Johnson put that engine inside pharmaceutical research programs, while internal oncology and immunology programs give it a route to develop assets of its own.
That distinction is meaningful, but it is not proof that Isomorphic makes better medicines. The strongest comparative evidence currently available is a company-authored February 10, 2026 technical report covering specific computational benchmarks. Those results should be read as task- and dataset-specific performance, not as clinical validation. Recursion and Insilico Medicine provide useful contrasts: Recursion emphasizes a closed loop of wet-lab experiments, proprietary data and machine learning, while Insilico explicitly offers a commercially available, end-to-end generative-AI and laboratory-automation platform. No public evidence in this comparison establishes a single overall winner.
What makes Isomorphic Labs different?
Most AI drug-discovery companies can be described through one of three emphases: a model that predicts biological or chemical properties, an experimental-and-data engine that repeatedly generates training data, or a software platform sold to research organizations. Isomorphic Labs publicly emphasizes a fourth formulation: a unified engine intended to connect several difficult prediction and design tasks inside one drug-discovery workflow.
Its platform is called the Isomorphic Drug Design Engine, or IsoDDE. Isomorphic describes it as target- and disease-agnostic and says it is intended to work across multiple therapeutic areas and modalities. The company’s public materials position the engine as an extension of the structural-biology progress associated with AlphaFold, not as a replacement for laboratory science or clinical development.
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Max Jaderberg, Isomorphic Labs’ president, has described AlphaFold 3 as “a major leap forward in understanding biological structures,” while adding that “we’re never going to solve drug design with AlphaFold alone.” That statement captures the company’s stated thesis: useful drug design requires predictions about how molecules interact, not only a static prediction of a protein’s structure.
One engine, several design questions
According to Isomorphic’s February 2026 technical report and technology descriptions, IsoDDE is intended to address questions such as:
- What three-dimensional configuration will a protein, antibody or other biomolecule adopt?
- How will a protein interact with a small molecule, another protein or an antibody?
- How strong is a predicted interaction, expressed as a binding-affinity estimate?
- Where might a ligand bind when the relevant pocket is not obvious from a conventional structure?
- Can the same computational approach be used across different targets, diseases and molecular modalities?
These are related problems, but they are not interchangeable. A model can predict a plausible pose without reliably estimating affinity, and a high-affinity prediction does not by itself establish selectivity, cellular activity, safety or clinical benefit. Isomorphic’s differentiator is therefore the claimed integration of these tasks, not a claim that any single prediction solves the entire development process.
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Structure and interaction prediction
IsoDDE is presented as going beyond structure prediction to molecular-interaction prediction. In practical terms, the engine is intended to model complexes involving proteins, ligands, antibodies and other biomolecules. The technical report evaluates several of these interaction categories, including protein–ligand, antibody–antigen and protein–protein cases.
This matters because drug discovery is usually constrained by the quality of a proposed interaction. Researchers need to know not just what a target looks like, but whether a candidate molecule can occupy a site in a chemically credible orientation and whether that interaction is likely to be strong enough and selective enough to justify laboratory testing.
Binding affinity
Binding affinity is an estimate of how strongly a molecule binds to its target. It is one of the inputs medicinal chemists use when deciding which compounds to synthesize or test. IsoDDE’s stated scope includes affinity prediction, but a computational estimate remains an estimate. Assay conditions, protein state, solvent effects, conformational changes and experimental error can all produce differences between a predicted and measured value.
Novel binding pockets
The company also says IsoDDE can identify novel binding pockets. A newly recognized pocket may expose chemical space that is difficult to reach with a conventional approach, especially for targets historically considered challenging. Finding a pocket is only the beginning: researchers still need to establish that it is biologically relevant, that a molecule can bind there selectively, and that modulating it produces a useful therapeutic effect.
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Multiple targets and modalities
Isomorphic describes the engine as usable across therapeutic areas and modalities rather than being dedicated to one disease family or one class of molecule. Its Johnson & Johnson collaboration publicly includes both small molecules and biologics. That breadth is a stated design goal, not evidence that every modality has reached the same level of maturity or that all targets perform equally well.
What do the published IsoDDE benchmarks show?
Isomorphic Labs’ technical report, dated February 10, 2026, reports results on several computational tasks. The company 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. The same report includes a FoldBench table with results for antibody–antigen, protein–ligand and protein–protein prediction.
The figures below are success rates under the report’s definitions and evaluation set. They are not clinical success rates, probabilities that a drug will reach approval, or independent rankings of the companies.
| FoldBench task | IsoDDE | AlphaFold 3 | Difference in reported score |
|---|---|---|---|
| Antibody–antigen | 75.58 | 47.90 | 27.68 points |
| Protein–ligand | 75.99 | 64.90 | 11.09 points |
| Protein–protein | 74.19 | 72.93 | 1.26 points |
All values in the table are reported by Isomorphic Labs in its 2026 technical report. The size of the apparent advantage varies substantially by task: it is largest for the antibody–antigen result, smaller for protein–ligand prediction and relatively narrow for protein–protein prediction. That variation is one reason to avoid turning the table into a blanket claim that IsoDDE is superior for every drug-design problem.
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How to interpret the Runs N’ Poses claim
The “more than twofold” statement applies to the hardest similarity category of a named protein–ligand benchmark. It does not mean that every candidate is twice as likely to become a medicine, nor that laboratory throughput, toxicity or clinical outcomes improve by the same factor. Benchmark categories, test-set composition and scoring definitions determine what a reported improvement actually measures.
What the benchmarks do not establish
- They do not demonstrate that an IsoDDE-designed compound has improved a patient outcome.
- They do not provide a head-to-head clinical comparison with Recursion, Insilico Medicine or any other company.
- They do not show that the model performs equally well on undisclosed partner targets or proprietary data.
- They do not remove the need for synthesis, biochemical assays, cell studies, animal studies, manufacturing work or clinical trials.
The report is valuable evidence about the tested computational tasks. It should be read alongside independent replication and prospective laboratory results before drawing conclusions about real-world drug-development performance.
How is Isomorphic Labs different from Recursion?
The clearest public difference is operating emphasis. Recursion’s description of Recursion OS joins physical wet-lab experimentation with computational analysis, proprietary data and machine-learning models. That is a feedback loop: experiments generate data, computational models analyze and prioritize the next experiments, and the resulting measurements feed the system again.
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Isomorphic’s public narrative foregrounds the unified computational design engine itself. Its technology descriptions emphasize in-silico prediction and interdisciplinary drug-design teams. Public partnership materials do not quantify how much wet-lab work Isomorphic performs internally, which laboratories conduct it, or how tightly each experiment is connected to model retraining.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problems| Comparison point | Isomorphic Labs | Recursion |
|---|---|---|
| Primary stated emphasis | Unified computational engine for biomolecular prediction and drug design | Integrated system linking wet-lab experiments, proprietary data and machine learning |
| Role of experimentation in public description | Drug-design and partnership programs are described; the public material reviewed does not quantify wet-lab integration | Physical wet-lab experimentation is explicitly part of Recursion OS |
| Publicly described output | Partnered and wholly owned therapeutic programs powered by IsoDDE | A platform and therapeutic pipeline built from the data-and-experiment loop |
| Evidence that can be compared here | Company technical benchmarks, partnerships and platform claims | Company description of the platform approach |
This is a comparison of stated operating models, not a verdict on effectiveness. A computationally unified platform may reduce disconnected handoffs between prediction tasks, while a large experimental loop may produce proprietary data that improves prioritization. The public material available here does not measure either advantage on a common clinical endpoint.
How is Isomorphic Labs different from Insilico Medicine?
Insilico Medicine describes Pharma.AI as a commercially available, end-to-end generative-AI software and automation platform. Its stated scope spans biology, chemistry, medicine and science. That description makes platform access and collaborations explicit parts of Insilico’s business model.
Isomorphic’s public materials instead emphasize using IsoDDE in strategic pharmaceutical collaborations and in internal therapeutic programs. They do not present the company primarily as a general-purpose software vendor offering the engine for unrestricted commercial use. This distinction is about how each company describes its offering; it does not establish that one model generates better candidates.
| Comparison point | Isomorphic Labs | Insilico Medicine |
|---|---|---|
| Core public positioning | Unified computational drug-design engine | Commercially available end-to-end generative-AI and automation platform |
| Stated coverage | Multiple therapeutic areas, targets and modalities | Tools across biology, chemistry, medicine and science |
| Commercial path described publicly | Strategic pharmaceutical collaborations plus internal programs | Platform access and collaborations |
| Evidence in the material considered here | Technical report, technology claims, partnerships and funding announcement | Official description of the Pharma.AI platform |
The practical question for a prospective partner is therefore different. An organization evaluating Isomorphic would ask how IsoDDE can be applied to a joint or internal program and what development work Isomorphic undertakes. An organization evaluating Insilico’s stated offering would also ask about software access, automation, integration and the division of responsibility between the customer and the platform provider.
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What do Isomorphic Labs’ partnerships reveal?
Novartis
Isomorphic says its collaboration with Novartis began in January 2024. The initial work targeted small-molecule therapeutics against three challenging targets, and the collaboration expanded in February 2025. The targets have not all been publicly identified.
Fiona Marshall, Novartis’ president of Biomedical Research, said the teams had seen “the exploration of new chemical spaces that would be unavailable to probe through traditional methods.” That is an attributed statement about the collaboration’s experience, not an independently measured demonstration that a resulting drug is effective.
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Eli Lilly
Isomorphic’s Lilly collaboration seeks small-molecule therapeutics against undisclosed targets. Because the targets and program data are not public, outsiders cannot use the announcement to compare target difficulty, candidate quality or development progress with another company’s portfolio.
Johnson & Johnson
The Johnson & Johnson collaboration spans multiple targets and modalities, including small molecules and biologics. That breadth is consistent with Isomorphic’s claim that IsoDDE is intended to generalize across modalities, but the announcement does not establish equal performance for each modality or disclose a complete program dataset.
Why partnerships matter—and what they cannot prove
Large pharmaceutical collaborations can provide disease expertise, target validation, medicinal-chemistry capacity, assays, regulatory experience and funding for expensive development steps. They also indicate that major research organizations are willing to test a relationship with Isomorphic. They do not prove that a candidate will be safe, effective or approved. Partner announcements often precede the disclosure of compounds, clinical data and go/no-go decisions.
Does Isomorphic Labs sell software or develop drugs?
Public descriptions show a mixed development model. Isomorphic uses its engine in strategic pharmaceutical collaborations and says it has wholly owned programs, including internal oncology and immunology work. In this model, the company is not simply licensing a prediction API and leaving all development to customers; it is also applying the technology to therapeutic programs.
That differs from a pure software model in which customers buy access to tools and run their own discovery workflows. It also differs from a traditional biotechnology company that relies mainly on an internally owned pipeline. A partnership can distribute cost and domain expertise, but it can also make program details, ownership terms and milestones less visible to the public.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What does the 2026 financing mean?
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.
The financing is a measure of capital raised, not a measure of drug efficacy. It can give the company more time and capacity to build models, generate data, hire specialists, run experiments and progress programs through costly development stages. It does not show that any particular candidate works in humans, and it should not be used as a substitute for clinical evidence.
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How should an organization compare AI drug-discovery companies?
A useful comparison separates technical claims from operating capability and from clinical evidence. The following questions prevent a benchmark score or financing headline from doing too much interpretive work.
| Evaluation axis | Questions to ask | What the public material establishes here |
|---|---|---|
| Technical emphasis | Is the company centered on one model, a unified design engine, an experimental data loop or a software suite? | Isomorphic describes a unified engine; Recursion describes a wet-lab and data loop; Insilico describes a broad commercial platform. |
| Task coverage | Does the system handle structure, interactions, affinity, pocket discovery, chemistry, biology or several of these? | IsoDDE is described as covering interaction prediction, affinity and binding-pocket identification in addition to structural tasks. |
| Modality coverage | Are claims limited to small molecules, or do they include antibodies, biologics and other modalities? | Isomorphic says the engine spans modalities; the J&J collaboration includes small molecules and biologics. |
| Wet-lab integration | Who runs experiments, how quickly do results feed back into models, and what proprietary data are generated? | Recursion explicitly includes wet-lab experimentation in Recursion OS; Isomorphic’s public materials do not quantify this integration. |
| Commercial model | Are customers buying software access, funding a collaboration, licensing assets or sharing development? | Insilico explicitly describes commercial platform access; Isomorphic emphasizes collaborations and internal programs. |
| Evidence quality | Are results independently replicated, prospective, published, and connected to clinical outcomes? | Isomorphic’s cited comparative results come from its own technical report; independent clinical comparisons are not established. |
| Program transparency | Are targets, molecules, assays, milestones and failures disclosed? | Several Isomorphic partner targets remain undisclosed, limiting external program comparison. |
Ask for prospective rather than retrospective proof
A retrospective benchmark can show that a model performed well on a defined test set. A stronger partnership evaluation asks the company to specify, before testing, how predictions will be selected, what assay will be used, what constitutes success and how many candidates will be advanced. Prospective protocols reduce the risk of choosing favorable examples after the results are known.
Separate discovery performance from development performance
Drug discovery contains multiple bottlenecks. A platform may improve pose prediction yet fail to produce selective compounds, or generate potent molecules that have poor exposure, toxicity or manufacturability. Compare companies separately on prediction accuracy, experimental hit rate, lead optimization, preclinical translation and clinical progression instead of compressing all stages into one “AI success” number.
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Public information does not provide a complete dataset for Isomorphic’s pipeline, a full list of partner targets, the number of compounds synthesized per program, prospective hit rates, attrition statistics or independent clinical outcome comparisons. It also does not establish how IsoDDE performs uniformly across diseases, target classes or modalities.
Those gaps are normal for active pharmaceutical programs, where confidentiality protects intellectual property and competitive position. They do mean that readers should distinguish between what Isomorphic says the engine is designed to do, what its technical report measured, and what remains unproven in human development.
Bottom line for readers comparing the companies
Isomorphic Labs’ defining public proposition is an integrated computational engine for drug design. IsoDDE is described as extending beyond structure prediction into interactions, affinity and novel-pocket discovery, with a goal of working across targets, diseases and modalities. The company’s reported FoldBench and Runs N’ Poses results provide concrete computational evidence, but they are company-reported and do not establish clinical superiority.
Recursion is the clearest contrast in operating emphasis because it describes a physical wet-lab, proprietary-data and machine-learning feedback system. Insilico is the clearest contrast in commercial model because it describes a commercially available platform spanning biology, chemistry, medicine and science. Isomorphic’s pharmaceutical partnerships and internal programs place it between a software supplier and a conventional pipeline biotechnology company.
The fairest conclusion is not that one company has won. It is that the companies are making different bets about where AI creates the most leverage: a unified prediction-and-design engine, an experimentally generated data flywheel, or broad commercial access to generative software and automation. Determining which bet produces safer and more effective medicines requires independent prospective experiments and clinical results that are not yet available for a like-for-like comparison.
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