Best AI platforms for drug discovery in 2026

R&D leaders, heads of AI, and bench scientists have a growing choice of AI platforms that promise to speed drug discovery, from the first target hypothesis to the studies that support an IND. Some teams are buying their first platform, while others already run one and want it to carry more of each program decision. Both groups need a fair way to compare the best AI platforms for drug discovery before committing a pipeline to one.

This article compares seven platforms, starting with EMET by BenchSci, on five criteria summarized in the table below. Three criteria concern what a platform contributes to a program: the data it brings, how much of the workflow it runs and connects, and the people who support it. The remaining two concern the vendor's own choices, namely whether you can pick the model for each task and whether the vendor develops drugs.

Table reflects each vendor's public materials as of October 2026.

What to look for in an AI platform for drug discovery

When you compare AI platforms for drug discovery, judge each one on five capabilities that shape how well it supports real target and candidate decisions.

• Proprietary, biology-specific evidence to reason over. A platform's evidence base is every paper, database, and curated dataset its agents can read before they answer. Target and safety calls often turn on findings in closed-access journals or in records of which reagents and models actually worked, so a platform limited to open sources can miss them. Ask each vendor what its agents read beyond PubMed and public databases, and whether any of that content is licensed or curated in-house.
• Depth of biology-grounded workflow and integration. A deep platform carries a program a long way on its own, from ranking targets on genetic evidence to profiling a lead compound's ADMET liabilities to designing the next study. Integration means it also connects to the systems your programs already run on, such as your ELN, your regulatory submission archive, or lab automation. Test it on one of your recent go or no-go questions and count the steps your team would still do by hand.
• A service model that drives adoption. After purchase, someone has to fit the platform to each program and therapeutic area. A platform that nobody adapts to the way your project teams work tends to stay a pilot. Ask who builds your custom workflows and connectors, whether those people are scientists assigned to your account, and whether they stay after launch.
• The freedom to use the best model for each task. A flexible platform can route each step, such as literature reasoning or antibody sequence analysis, to the model that handles it best. The leading model changes every few months, and a platform built on one provider inherits that provider's pricing and roadmap. Check which model providers a platform supports and how quickly it adds new ones.
• A clear position on whether the vendor develops its own drugs. A vendor with its own drug program, or plans for one, could end up competing with yours. Your targets, assay data, and failed programs will pass through the platform, and that information is valuable to anyone with a pipeline. Ask for the vendor's position in writing, along with its policy on training models with customer data.

In practice the five capabilities form one chain: the evidence shapes what the agents conclude, the workflows turn those conclusions into analyses and study designs, and the team keeps the workflows matched to each program. Model flexibility keeps the reasoning current, and neutrality decides which programs you can safely run through the platform at all. The seven platforms below cover different parts of that chain, and they leave different amounts for your own team to build.

1. EMET by BenchSci

Designed solely for biopharma R&D, EMET by BenchSci is an AI platform whose deepest coverage runs from target identification and validation through lead optimization, safety, and IND-enabling studies. BenchSci was founded in 2015 after a PhD student lost rare patient samples to a bad antibody and the experimental evidence behind that kind of reagent choice remains central to the platform. EMET opens this list because it combines biology data other platforms cannot reach with PhD scientists embedded in every deployment.

What EMET does well

Five capabilities define EMET, and they follow the same order as the criteria above.

Start with the evidence behind a target decision: EMET's agents reason over a biological knowledge graph of 858M nodes and 2.2B relationship edges, built from 38M+ publications. That includes 16M closed-access papers BenchSci licenses through eight years of publisher partnerships with Elsevier, Springer Nature, Wiley, Oxford University Press, and others. Across 20 preclinical questions spanning disease biology to translation, EMET with its knowledge graph scored 93.0, against about 75 for the top frontier model queried without that knowledge layer. A reagent and model-system database curated by 60 PhD scientists adds records on 500K cell lines and 700K animal models for planning the experiments that test that decision.

Next is workflow depth. More than 250 agents and workflows, built on 200+ proprietary scientific skills, run discovery workflows such as genetics-backed target ranking and preclinical workflows such as ADMET profiling of a lead series. In September 2025, BenchSci and Thermo Fisher Scientific announced a strategic partnership to build AI tools for experimental design and reagent selection on BenchSci's technology.

EMET connects to a customer's internal data and to automated lab infrastructure, so a study designed in EMET can move toward execution.

Third, every deployment includes dedicated PhD scientists embedded with the customer. They learn the organization's therapeutic areas, train its researchers, and build the custom workflows and connectors each program needs. A separate value engineering team measures and reports the platform's impact over time.

Fourth, because BenchSci trains no foundation model of its own, EMET routes each task across 40+ models. It can hand open reasoning to a frontier language model and a sequence question to a specialist such as ESM-2 or AbLang2. It adds new frontier models as they ship, so no program depends on one provider. Through a multi-year partnership with Mila, BenchSci is also developing inference models for hypothesis generation and assay prediction on top of its knowledge graph.

The fifth is neutrality: BenchSci will never develop drugs of its own, so the targets and assay results a team brings to EMET cannot end up in a BenchSci pipeline. Each organization's data stays isolated to that organization.

Where EMET falls short

EMET's deepest coverage stops near the transition to the clinic. Teams whose main problem is running trials or preparing a commercial launch will find narrower support than discovery and preclinical teams get.

EMET is priced for enterprises. Companies license the full platform, including the closed-access literature and lab connectors, through an enterprise agreement. For a small biotech running one or two programs, that can be a heavy commitment.

Wiring EMET into a company's own systems is a joint project with IT. Linking it to internal data, an ELN, or lab automation depends on the teams that manage those systems, so reaching full integration takes longer than starting to use the platform.

Best for

Biopharma R&D organizations whose target, safety, and candidate decisions rely on evidence beyond public sources will find EMET the clear choice. It suits discovery and preclinical teams that want every recommendation cited to its source before it reaches a portfolio review, with scientists on the vendor side building workflows around their programs.

2. Claude Science

Anthropic released Claude Science on June 30, 2026, as a beta app for paid Claude plans. Scientists install it where their data already lives, whether a laptop, a lab server, or an HPC login node, and work with a coordinating agent backed by 60+ preconfigured skills and connectors. Biotech and academic teams have used it for single-cell analysis, CRISPR screen design, and protein structure prediction.

What Claude Science does well

Early target nomination is one documented use. Anthropic's launch post describes Manifold Bio using the app to nominate targets for tissue-targeting medicines, assessing surface expression, trafficking, and safety for each candidate. The app then ranked those candidates against criteria Manifold had learned from its own proprietary data.

It connects to sources a drug discovery team already uses. The Claude Science product page shows connectors for Cortellis, ChEMBL, and Benchling, and it lists fully sourced indication dossiers among the app's capabilities. Data partners extend that reach, for example Helix, which offers its linked clinico-genomic dataset inside the app through an MCP server.

Where Claude Science falls short

Anthropic is building drug discovery work of its own. In a September 2026 post introducing its new biology lab, Anthropic said its life sciences organization includes teams whose work includes drug discovery. MIT Technology Review reported from the launch that Anthropic plans to run its own research on drug candidates for neglected diseases with the app.

Its reasoning runs on one provider's models. According to Anthropic's FAQ, Claude Science is an app that runs on the Claude models in a customer's existing plan, while open biology models from NVIDIA's BioNeMo library, including Evo 2 and Boltz-2, plug in as tools. As of October 2026, Anthropic does not describe sending reasoning steps to frontier models from other companies.

Its evidence comes from public databases, partner data, and each lab's own files. Specialist agents query public resources such as UniProt, ClinVar, and ChEMBL, along with journals and preprint servers. As of October 2026, Anthropic does not describe a licensed or curated biology dataset of its own behind the app.

Best for

Computational biologists at biotechs and academic labs will get the most from Claude Science if they want to run target and genomics analyses on their own hardware with a general-purpose AI model. It suits teams already on paid Claude plans that want reproducible figures and connectors to common drug discovery databases without a separate purchase.

3. Causaly

Computer scientists Yiannis Kiachopoulos and Artur Saudabayev founded Causaly in 2018, and the company is headquartered in London. Its agentic AI platform reasons over a scientific knowledge graph holding 500M facts linked by 70M directional relationships, alongside a commercial graph of the drug landscape. Pharma companies such as Ipsen, UCB, and Eisai use it for work that ranges from target identification to indication expansion and regulatory evidence.

What Causaly does well

Causaly has a documented record in target identification. In a ProQR case study, Causaly reports that ProQR met its 2024 target ID goal by the third quarter, and that half the targets ProQR pitched that quarter drew on Causaly insights. ProQR also used it to surface safety concerns that informed go or no-go calls during target validation.

It ties the biology to portfolio decisions. Pipeline Graph, launched in June 2025, combines pipeline and competitive data with preclinical evidence so teams can compare programs and judge a target's commercial position. After configuring a Scientific Workflows indication assessment, a top-50 pharma company cut up to a month of manual research from each indication it assessed, according to Causaly.

Causaly offers professional services from PhD scientists and from science and change management specialists, covering deployment planning through workflow execution and ongoing research. It shows no drug program in its public materials as of October 2026. Its enterprise page also commits that client data is never used to train Causaly's models.

Where Causaly falls short

Causaly's evidence is the published record plus each customer's own data. Its knowledge graph draws on published literature, patent filings, and clinical trial records, and customer data can be added securely on top. Its public materials do not describe a curated dataset of reagent or model-system results as of October 2026.

Its workflows center on evidence retrieval, reasoning, and decision support. Running omics pipelines or docking studies is not described as a native Causaly capability as of October 2026. In its June 2026 Microsoft collaboration, Microsoft Discovery provides the computation and lab automation integrations while Causaly supplies the interpretation.

Its scientist support is organized around rollout and enablement. Causaly describes its services in terms of deployment planning, adoption, and ongoing research enablement. As of October 2026, Causaly does not publicly describe assigning a standing team of scientists to each customer account.

Best for

Causaly fits target, indication, and portfolio teams that need cited assessments of the published evidence alongside competitive pipeline context. It suits organizations that want repeatable assessment workflows with scientist support, and that run heavy computation in a separate tool.

4. OpenAI Rosalind

OpenAI sells its life sciences products under the Rosalind name, which covers the GPT-Rosalind model and the Rosalind Workbench app. The company launched GPT-Rosalind in April 2026 for research across biology, drug discovery, and translational medicine. Since September 11, eligible organizations worldwide can use it through a trusted-access program, and Novo Nordisk and Amgen are among the drug developers working with it.

What OpenAI Rosalind does well

GPT-Rosalind is trained for medicinal chemistry as well as biology. In a June 2026 update, OpenAI reported that GPT-Rosalind scored 27.5% on MedChemBench, against 25.1% for GPT-5.5. The benchmark tests structure-activity reasoning, ADME and toxicity prediction, and lead-optimization decisions.

It supports target work directly. Rosalind Workbench and Codex can gather evidence in parallel to score and rank candidate targets. OpenAI's help center lists target discovery, target validation, and experiment planning among the model's uses.

OpenAI supplies models and tools to drug developers, and its public materials describe no internal drug program as of October 2026. Its help center says enterprise customer data is not used for training by default.

Where OpenAI Rosalind falls short

Rosalind is built on OpenAI's own models. GPT-Rosalind powers Research mode, while Explore mode draws on whichever ChatGPT models the user's plan includes, and the Codex research plugin pairs with OpenAI's mainline models. Teams that build their drug discovery workflows on Rosalind tie them to one provider's model roadmap.

Its evidence comes mainly from public sources. The Life Sciences research plugin connects to more than 50 public multi-omics databases, literature sources, and analysis tools. As of October 2026, OpenAI's help center describes the use of a company's own data as in scope but not yet a standard feature, with some workflows needing custom setup.

Its coverage is concentrated early in the pipeline. OpenAI describes the model as most valuable in early discovery, for generating hypotheses, and Rosalind Workbench is still a research preview, starting with guided sequencing analysis. Its public materials do not describe IND-enabling workflows or connections to lab automation as of October 2026.

Best for

Computational biologists and medicinal chemists at organizations already on ChatGPT Enterprise or Codex are the natural users of OpenAI Rosalind. It suits early discovery teams that want a model trained in biology and chemistry for target hypotheses and lead ideas, and that have the engineering staff to build workflows around it.

5. Microsoft Discovery

Microsoft Discovery is an Azure platform where organizations build and govern AI agent workflows for science and engineering, including pharmaceutical research. After a private preview that began at Build 2025, the platform reached general availability on June 2, 2026, together with a preview desktop app. Organizations working with it include GSK, the mining company BHP, and the science company Syensqo.

What Microsoft Discovery does well

Model choice stays with the customer. Microsoft describes Discovery as an open, extensible platform where teams bring the models, tools, and data they prefer, with Microsoft Foundry and Azure HPC available underneath.

It is built for candidate screening and closed-loop experiments. Microsoft lists screening thousands of potential drug candidates among its core scenarios, and at Pacific Northwest National Laboratory the platform connects to laboratory automation for self-driving biology experiments. Ginkgo Bioworks is also working with Microsoft so researchers can plan experiments in Discovery and run them on Ginkgo Cloud Lab, a capability the two companies describe as coming soon.

Microsoft acts as a platform provider, and its public materials describe no drug pipeline as of October 2026. Its platform card keeps each customer's data, knowledge graphs, and models inside that customer's own tenant, and customers retain ownership of those assets.

Where Microsoft Discovery falls short

Its biology evidence depends on what customers and partners bring. The Discovery Engine builds knowledge bases from a customer's documents and scientific literature, and partners such as Wiley add agents, including one that indexes more than one million life sciences articles. As of October 2026, Microsoft does not describe a biology dataset that it curates itself.

Drug discovery workflows are left for each team to assemble. Battery researchers, mining companies, and pharma teams all use the same platform, and each organization designs its own agent workflows for its science. On its key scenarios page, Microsoft still marks lab-integrated experimentation as emerging.

Delivery runs through partners and the customer's own staff. Microsoft's ecosystem includes firms such as Cambridge Consultants, and the platform card makes customers responsible for securing their own subscriptions, networks, and access policies. As of October 2026, Microsoft does not describe assigning its own scientists to each account.

Best for

Microsoft Discovery fits Azure-standardized organizations with platform engineers ready to design agent workflows themselves. It suits companies where drug candidate screening sits next to chemistry or materials work, and where partners or internal teams will configure the platform.

6. FutureHouse (Edison Scientific)

Sam Rodriques and Andrew White started FutureHouse in 2023 as a San Francisco nonprofit building AI for science. Edison Scientific, which FutureHouse spun out in 2025 as a for-profit, now sells those AI systems on its platform, led by an AI scientist called Kosmos that runs research workflows from target identification through clinical development and filing. In June 2026, Edison also announced a partnership with Population Health Partners to co-build biotechs, starting with a clinical-stage asset from PHP's portfolio.

What FutureHouse does well

Its agents have a published drug discovery result. Robin, a multi-agent system built at FutureHouse, proposed ripasudil, a glaucoma drug, as a candidate for dry age-related macular degeneration, and the team confirmed its effect in cell assays in work published in Nature in 2026. Incyte has since embedded Kosmos in its target discovery, target validation, and translational biology work.

Kosmos also reaches the regulatory end of preclinical work. In a Population Health Partners case study, Kosmos produced a first acceptable draft of a Nonclinical Overview in 4.2 hours, against roughly 100 hours of expert time. About 70% of the Pre-IND Briefing Meeting Request that PHP submitted to the FDA was drafted by Kosmos and then edited by PHP.

Edison staffs and adapts each deployment. Edison's homepage lists forward-deployed scientists who join customer teams, along with custom world models built on each customer's data. Edison also keeps Kosmos model agnostic, adopting new frontier models as they appear.

Where FutureHouse falls short

Its licensed literature is still being assembled. Edison's Literature agent searches a corpus of more than 175M papers, clinical trials, and patents, and a September 2026 agreement brought Springer Nature's Nature portfolio into Kosmos as the first of several planned content partnerships. Its public materials do not describe a curated dataset of reagent or model-system results as of October 2026.

Its connections to lab systems are less defined. Edison describes Kosmos learning from an organization's experimental history and working with scientists through Slack, Teams, and email. As of October 2026, Edison's public pages list no connectors for ELNs, regulatory systems, or lab automation.

Best for

FutureHouse's tools, sold through Edison Scientific, fit teams that want long autonomous runs over the literature and their own data, plus help drafting the documents that lead to an IND. They suit organizations that want forward-deployed scientists to build world models around their programs.

7. Phylo

Phylo spun out of Biomni, an open-source Stanford research project, and describes itself as an applied AI research lab. Its current product, Biomni Lab, arrived in February 2026 alongside a $13.5 million seed round co-led by Andreessen Horowitz and Menlo Ventures' Anthology Fund. According to Phylo, Biomni has 74K+ users across 7K+ organizations, including teams at Ono Pharmaceutical and Chugai.

What Phylo does well

Biomni Lab covers computational work across the early stages of a program. Its enterprise page maps workflows from human genetics and target validation through virtual screening and preclinical safety prediction, such as hERG liabilities. A Penn researcher quoted there used it to screen tens of thousands of compounds against crystal structures and shortlist leads for lab testing.

It also packages target prioritization for R&D leaders. Its Target Atlas app weighs genetic, expression, and druggability evidence to rank targets for a disease, and it keeps every score traceable to the underlying data. Phylo built a Parkinson's edition with the Michael J. Fox Foundation's Targets to Therapies initiative.

Phylo leaves model choice and data control with the customer. It describes Biomni Lab as model-agnostic, keeps customer data out of model training, and shows no drug pipeline of its own in its public materials as of October 2026. Its enterprise tier includes white-glove onboarding and support, along with deployment in a customer-hosted VPC.

Where Phylo falls short

Phylo's evidence comes from public and partner resources. Its launch announcement describes 300+ databases, software systems, and analytical tools, plus partner sources including COSMIC and Addgene, and enterprise deployments add each customer's data, as in the Chugai deployment. As of October 2026, Phylo does not describe a licensed or curated biology dataset of its own.

Its workflows center on in silico analysis. Analyses run inside Biomni Lab's own workspace, and enterprise plans list integrations with internal data, tools, and workflows. As of October 2026, Phylo's public materials name no connectors for ELNs or lab automation systems.

Its hands-on support depends on the plan. White-glove onboarding and support come with the Enterprise plan, while the Free, Pro, and Team tiers start with self sign-up. Its public materials, as of October 2026, describe no standing team of scientists dedicated to each customer account.

Best for

Phylo fits discovery and medicinal chemistry scientists who want to run screening, genetics, and ADMET analyses themselves, starting on a free tier. It suits biotech and academic teams that want broad computational coverage of early drug discovery without a large deployment.

Which AI platform for drug discovery is right for you?

For target analyses run with a general-purpose AI model on your own infrastructure, Claude Science may be enough. If your chemists and computational biologists already use ChatGPT Enterprise or Codex, OpenAI Rosalind is worth a look for early target and lead hypotheses.

When the priority is literature-backed target and indication assessments with pipeline context, Causaly is worth a look. Microsoft Discovery may be enough for candidate screening workflows that your own engineers or partners will build on Azure.

Teams seeking long autonomous research runs and help drafting pre-IND documents should look at FutureHouse's tools through Edison Scientific. For self-serve screening and ADMET analysis by individual scientists, Biomni Lab from Phylo may be enough.

For programs where a go or no-go call rests on closed-access evidence, your own experimental records, and workflows built around your teams, choose EMET by BenchSci, which is designed for that decision. Book a demo to watch it work through a target from your own pipeline.