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AI drug discovery is not one kind of software: some platforms help researchers choose targets or design molecules, while others focus on proteins, chemical analysis, or broader development workflows. These seven examples show how different the tools are—and why company-reported usage, model benchmarks, and evidence from clinical trials should not be treated as interchangeable.
What does AI do in drug discovery?
Drug research involves a chain of decisions, from selecting a biological target to finding and testing a candidate, then evaluating it in people. AI systems can support particular steps by analyzing data, generating predictions, or proposing candidates. A platform’s usefulness therefore depends on the task, data, and experimental follow-up—not on the label “AI drug discovery” alone.
The seven platforms below are a selection, not a ranking or a claim that these are the only leading systems. Their stated scopes and evidence come largely from the companies themselves. Where a company describes a capability or performance claim, it is attributed rather than presented as an independently established result.
Which seven AI drug discovery platforms are being used?
| Platform | Stated focus | Operating model | Evidence or scale reported |
|---|---|---|---|
| Lilly TuneLab | Access to AI/ML models and data for biotech research | External research platform with partner contributions | Platform-reported users, datasets, model runs, and contributed data |
| Recursion OS | Discovery and development | Integrated platform used within Recursion’s operations | Company claims on speed, efficiency, and cost compared with traditional pharmaceutical-company averages |
| Insilico Medicine Pharma.AI | Target identification, small-molecule generation, and clinical-outcome prediction | AI platform described in a company disclosure | Company disclosure of pharmaceutical-company collaborations |
| Inductive Beacon AI and Indy | Chemical intelligence and medicinal-chemistry workflows | Tools for research teams | Vendor-described functions and benchmark claims |
| Cradle | Protein design | AI tools trained on customer data | Company-reported pharmaceutical customers |
| AlgoraeOS | Drug-combination discovery | Platform developed with research partners, according to the company | Company-reported training data and independent validation |
| Anew Labs | Target identification through clinical development | Integrated platform, according to the company | Company description of scope; clinical performance is not established by that description |
Lilly TuneLab: models and data for external biotech teams
Lilly describes TuneLab as a way for biotech researchers to access Lilly AI/ML models as well as other models, with partner contributions also part of the platform. Lilly says its models were trained on more than 2 million of its preclinical data points. The company’s 2025 launch announcement said more than $1 billion in research investment underlies the models; that figure describes the investment Lilly associates with them, not a measured return or drug-development outcome.
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The current TuneLab platform page reports 125+ member companies, 500+ users, 173 datasets, and 9,900+ model runs. Its summary metrics list 600,000+ partner-contributed data points, while descriptive text reports more than 675,000 as of September 2026. These are platform-reported scale figures, and the page’s two partner-data totals are not identical. None of these counts establishes that a model produced a successful drug.
Recursion OS: an integrated discovery-and-development operation
Recursion presents OS as an integrated platform spanning drug discovery and development, rather than simply a standalone tool for outside teams. The company claims improvements in speed, efficiency, and cost relative to traditional pharmaceutical-company averages. Those comparisons should be read as Recursion’s claims: the cited description does not establish a shared baseline, the workflows included, or a head-to-head result that applies to other companies.
Rank #2
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Insilico Medicine Pharma.AI: targets, molecules, and clinical predictions
A 2025 Hong Kong Exchange filing describes Pharma.AI as covering target identification, small-molecule generation, and clinical-outcome prediction. The filing says Insilico collaborated with 13 of the 20 largest pharmaceutical companies ranked by 2024 sales. That is a company disclosure about collaborations; it does not show that every collaborator used every Pharma.AI component or establish the performance of a particular drug candidate.
Inductive Beacon AI and Indy: chemical analysis and medicinal chemistry
Inductive describes Beacon AI and Indy as tools for chemical intelligence and medicinal-chemistry work. Its listed functions include assay-data quality control, synthesis-queue monitoring, free-energy perturbation (FEP), and human-dose projection. These functions address different decisions, so a result in one does not establish performance in the others. Inductive also promotes benchmark results on its current homepage; without an independently inspected benchmark and its conditions, those claims should not be read as a universal ranking.
Rank #3
Cradle: protein design using customer data
Cradle says its protein-design tools are trained on customer data. In a 2025 company update, it reported serving six of the top 25 pharmaceutical leaders. That customer figure is company-reported evidence of adoption, not a measure of how many designed proteins worked in experiments or became medicines.
AlgoraeOS: drug-combination discovery
Algorae Pharmaceuticals describes AlgoraeOS as a platform for discovering drug combinations. The company says it was developed with the UNSW AI Institute and CSIRO Data61, independently validated with Peter MacCallum Cancer Centre, and trained on more than 5.5 million unique inhibition records. Those are company-page claims; the number of records describes training-data scale, not clinical validation or treatment success.
Rank #4
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Anew Labs: an end-to-end development scope
Anew Labs describes its platform as extending from target identification through clinical development. That breadth is the company’s positioning. A stated scope across development stages does not, by itself, demonstrate that the platform has improved clinical outcomes or that it performs every stage autonomously.
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The clearest distinction is whether a system offers capabilities to external research teams or forms part of an integrated company operation. Lilly explicitly describes external model access; Recursion presents OS as part of its own integrated discovery-and-development platform. The other company descriptions cover distinct tasks or broad scopes, but the available descriptions do not establish comparable access terms, pricing, or a common way to measure performance.
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- Large volume capacity: 250mL size ideal for cell harvesting, purification, and drug discovery
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- Safe and sterile: Tubes are free from RNase, DNase, and pyrogens, ensuring the utmost purity for your samples
- Leak-proof design: Secure screw-cap seal prevents leakage and maintains sample integrity, withstanding up to 6,000xg maximum RCF*
Modality and output matter just as much. Protein design is not the same task as small-molecule generation, drug-combination discovery, or chemical-property analysis. A benchmark for one output cannot serve as a fair score for all the others. Likewise, a customer count answers a different question from an experimental validation result, and neither alone answers whether a candidate succeeds in clinical testing.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Can AI really speed up drug discovery?
AI can help researchers prioritize possibilities, analyze data, or generate candidates, but a prediction is not a tested medicine. Candidates still need the relevant experimental work and, where they progress, preclinical and clinical evaluation. The platform descriptions here do not provide a neutral, head-to-head dataset showing that AI-designed candidates have higher clinical success rates than conventional candidates, or that AI alone guarantees faster approvals.
Recursion’s speed and cost comparisons are company claims, and they depend on what baseline and workflow are being compared. Platform usage figures from TuneLab, customer disclosures from Cradle or Insilico, and Algorae’s reported training records indicate scale or adoption; they are not substitutes for evidence that a particular treatment works.
The Tool Desk
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Quick Recap
What should a biotech team evaluate before choosing a platform?
- Fit to the decision: Identify whether the immediate need is target selection, molecule or protein design, chemical analysis, combination discovery, or a broader workflow.
- Data provenance and access: Ask what data a model was trained on, whether customer data is involved, and what the team can actually access. The platform descriptions do not provide a comparable account of data rights or access conditions.
- Validation conditions: Separate vendor benchmarks from independent validation, and ask what was measured, on what data, and against which baseline.
- Experimental handoff: Establish how predictions will be tested in the lab and what decisions remain with researchers. AI output does not remove the need for relevant experimental and clinical work.
- Evidence at the right level: Treat usage, partnerships, model metrics, experimental findings, and clinical outcomes as distinct evidence categories rather than one general measure of success.
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