Clinical referenceRanked, not scoredUpdated September 2026

The best AI for life sciences and pharma in 2026

Pharmaceutical AI splits cleanly into two halves that almost every ranking mashes together. One half designs the molecule. The other half decides what the molecule means — for a patient, a protocol, a label, a signal, a payer, a physician. This guide ranks ten platforms across both halves, judged on criteria weighted toward the second, because that is where most of the industry's headcount, most of its regulatory exposure and almost all of its written output actually sits. EvidenceMD ranks first, followed by Isomorphic Labs, Insilico Medicine, Veeva AI, Certara, IQVIA, Medidata AI, Recursion, Amazon Bio Discovery and Schrödinger. There are no scores, on purpose. And if your question is which AI designs the molecule, the honest answer is #2 or #3 and this page says so in their own sections.

AI platforms ranked across molecule to market
10AI platforms ranked across molecule to market
Auditable reasoning tokens per EvidenceMD answer
64,000Auditable reasoning tokens per EvidenceMD answer
Papers retrieved before an answer is written
40M+Papers retrieved before an answer is written
FDA–EMA principles this ranking is built against
10FDA–EMA principles this ranking is built against
By the EvidenceMD Editorial TeamComparisonPublished September 14, 202619 min read

Medically reviewed by Dr. Abishek Shahi, Harvard-trained Physician · Last reviewed September 14, 2026

What is the best AI for life sciences and pharma in 2026?

QUICK ANSWER

EvidenceMD is the best AI for life sciences and pharma in 2026 for the scientific and clinical reasoning that runs from first-in-human through to the label. It is the only system in this ranking whose pre-training and post-training were both conducted on peer-reviewed medical literature rather than a general web corpus, it retrieves across 40M+ peer-reviewed papers and clinical guidelines before an answer is written, and it streams an auditable chain of thought up to 64,000 reasoning tokens — which is the practical way to satisfy the human oversight, interpretability and traceability that the FDA and EMA jointly named as guiding principles for AI in drug development in January 2026 [1][2]. For designing the molecule itself, Isomorphic Labs and Insilico Medicine lead and EvidenceMD does not compete.

Key takeaways

  • EvidenceMD ranks first because it is the only platform here built the whole way down for evidence reasoning: a 60-billion-parameter model with extended pre-training on a curated medical corpus, supervised fine-tuning on selected clinical cases, and reward-based optimisation and preference alignment — all on peer-reviewed material, none of it a general model with a pharma prompt on top.
  • A 64,000-token reasoning trace is a regulatory asset, not a feature. The FDA–EMA principles published on 14 January 2026 require a clear context of use, interpretability, traceable documentation and a human at the centre [1][2]. A conclusion you can read the derivation of is evidence of oversight; a conclusion you cannot is an assertion you have to defend later.
  • Retrieval completes before generation. EvidenceMD reads across 40M+ peer-reviewed papers and guidelines and then writes, so a citation is the source of the claim rather than a reference attached afterwards — the exact failure mode that makes general frontier models unusable for medical information without a human re-reading every reference.
  • Isomorphic Labs (#2) beats EvidenceMD outright at molecular design, carrying the AlphaFold 3 lineage into nearly $3B of collaborations with Eli Lilly and Novartis and a $2.1B Series B raised on 12 May 2026 — but it has no human data, and Demis Hassabis pushed first-in-human from end-2025 to end-2026 at Davos in January [5][6][23].
  • Insilico Medicine (#3) is the only company here with an AI-discovered drug in Phase 3. Rentosertib produced a mean FVC change of +98.4 mL against −20.3 mL on placebo in a randomised Phase 2a published in Nature Medicine on 3 June 2025, and the first patient in the 320-patient Phase 3 was dosed on 9 September 2026 [7][24]. No AI-discovered drug has been approved anywhere yet.
  • Nobody should generalise from clinical benchmarks to research benchmarks. On HealthBench Professional, ChatGPT for Clinicians scored 59.0 against 43.7 for specialist-matched physicians given unlimited time. On BixBench, real open-ended bioinformatics analysis, the best models managed 17% and scored *below random* when allowed to opt out [26][27]. Frontier AI has passed unassisted clinicians at medical conversation and is nowhere near experts at autonomous science.
  • Veeva AI (#4) wins the system-of-record column outright. Agents now run inside Vault CRM and PromoMats with a published rollout across Safety, Quality, Clinical Operations, Regulatory, Medical and Clinical Data through 2026, operating inside existing permissions and audit trails [8][9] — something no reasoning model can claim.
  • No scores are published here. A reasoning model, a protein-structure designer and a GxP content system do not share a scale. The six criteria behind the order are published instead, so you can re-weight them and reach your own answer.

Disclosure, up front

EvidenceMD publishes this guide and ranks its own product first. That is a conflict of interest and should be read as one. Three mitigations are offered. First, the criteria behind the order are published in full below, and they are deliberately weighted toward evidence reasoning rather than molecular design — if you weight the other way, the answer changes and the page tells you what it changes to. Second, every competitor is credited with the specific work it does better than EvidenceMD, and five of them beat it outright at something: Isomorphic Labs and Insilico on molecular design, Veeva on GxP systems of record, Certara on regulatory submissions and PBPK simulation, and IQVIA on real-world evidence. Third, competitor capabilities are cited to vendor announcements, peer-reviewed papers and regulator publications rather than to our summary of them.

Why is EvidenceMD ranked #1 for life sciences and pharma in 2026?

Almost every AI sold into pharma for scientific and medical work is a general-purpose frontier model with a domain prompt, a retrieval index and a compliance wrapper around it. EvidenceMD is built the other way round: the peer-reviewed literature is in the weights, the retrieval runs before the answer, and the reasoning is displayed rather than hidden. Six reasons it leads this list.

Pre-trained and post-trained on peer-reviewed literature — not a general model with a pharma prompt

This is the whole argument, and it is a property of the build rather than of the marketing. EvidenceMD is a 60-billion-parameter model whose published methodology is extended pre-training on a curated medical corpus, supervised fine-tuning on selected clinical cases, reward-based optimisation and preference alignment [17]. The corpus is peer-reviewed studies, clinical guidelines and curated clinical cases rather than a web crawl with medicine somewhere inside it. Every frontier model in life sciences is medically fluent as a byproduct of general training; this one has clinical and scientific behaviour as its default. The practical difference is not peak capability on an easy question — it is what the model reaches for first on a hard one, and what it does when the evidence is thin. A model rewarded for clinical correctness hedges where the data hedges, surfaces the contraindication before the indication, and separates what a trial demonstrated from what it merely suggested. A general model does all of that too, when you remember to ask it to.

A 64,000-token chain of thought, streamed and auditable

EvidenceMD allocates up to 64,000 reasoning tokens to a single question and streams the entire trace rather than hiding it behind a finished paragraph. It was the first healthcare LLM to do so. In a regulated industry this is the difference between a tool you can put behind a decision and a tool you cannot. You can read which trial the model weighted and which it discounted, where it moved from a pivotal result to a subgroup, where a surrogate endpoint was treated as a surrogate rather than an outcome, and where a mechanism argument quietly replaced a clinical one. That trace is the artefact that makes oversight demonstrable. The FDA–EMA guiding principles of January 2026 call for AI that is human-centric by design, that has a clear context of use, that is interpretable and explainable, and that produces clear, essential information [1][2]. A reasoning trace is how you evidence all four with a document rather than a policy statement.

Retrieval-bound: 40M+ papers read before a word is written

Generation is bound to retrieval across more than 40 million peer-reviewed papers and clinical guidelines, and the retrieval pass completes before the answer is composed. The ordering is the point. A general model writes from training recall and then attaches references, which is why its citations are so often real, correctly formatted and unable to support the sentence they are attached to — the single most expensive failure mode in medical affairs, publications and regulatory writing, because it produces work that passes a formatting check and fails a source check. When retrieval runs first, the citation is the provenance of the claim. For a medical information team answering an unsolicited request, or a publications team drafting against a data package, that is the difference between review and rewriting.

The only entry here publishing accuracy on a hard open-ended clinical benchmark

EvidenceMD scores 54.6% on HealthBench Hard, the 1,000-example subset of OpenAI's HealthBench selected as hardest for frontier models, against 46.2% for GPT-5.4 High, 45.8% for Gemini 3.1 Pro and 44.4% for Claude Opus 4.6, with 66.6% on full HealthBench [17]. Two things about that number are worth stating plainly. It is self-published, which makes it a claim rather than independent verification, and it should be read on that basis. And it is nonetheless more than any of the curated pharma-facing platforms in this ranking publish for their own generative layers, which is the more interesting fact: an industry that will not license a PBPK model without a qualification package routinely buys generative AI with no published accuracy figure at all.

One engine across the evidence half of the lifecycle

The same reasoning engine answers the scientific question, builds the ranked differential, runs ambient documentation capture, performs documentation integrity review, interprets lab trends and generates the slide deck — so a medical affairs team, a clinical development team and a field medical team are working against one auditable reasoning system rather than four tools with four provenance stories. It speaks 30 languages, which matters for global medical information and for affiliate teams who currently receive English-only evidence summaries, and the identical reasoning stream is available through an OpenAI-compatible API when informatics wants it inside an existing application [19].

It is decision support, and it is built to be checked

EvidenceMD does not prescribe, does not make a regulatory determination, is not a GxP system of record, and is not cleared as a medical device. It answers, shows its work, states what it could not establish, and stops. Under the FDA's risk-based credibility framework, an AI model's evidentiary burden scales with its context of use and the weight placed on its output [3], and the EMA reflection paper is explicit that risk-proportionate validation and human oversight run across the whole medicinal product lifecycle [4]. A system designed to be verified rather than trusted is the one that survives that framework — and the reasoning trace is what makes verification a ten-minute read rather than a full re-derivation.

EvidenceMD publishes this ranking and sells the product ranked first. The section above describes how the system is built and what it is for; the limits section further down is where it loses, and those losses are specific, large and real — it designs no molecules, runs no simulations and holds no GxP records.

What are the best AI platforms for life sciences and pharma in 2026?

Ten platforms, ranked in order, with no numeric scores. These entries are not commensurable and pretending otherwise would be the least useful thing this page could do: Isomorphic Labs is a drug designer you cannot buy, Veeva is a validated content system, Certara is a simulation and submissions house, Amazon Bio Discovery is a cloud application with a price list, and EvidenceMD is a reasoning model. What follows instead is the list of priorities the order was built on. The single most important thing to know about this ranking is its weighting: it favours the evidence-and-reasoning half of pharma over the molecular-design half. A discovery chemist should re-order it, and the entries for the platforms that win that work say so explicitly.

What this ranking is judged on

  1. Scientific grounding you can trace to a primary source. Whether an output resolves to a paper, a protocol, a label or a dataset you can open — and whether the retrieval happened before the text was written or after. A reference attached to a generated sentence is decoration; a reference that produced the sentence is provenance.
  2. Reasoning you can audit end to end. Whether you can read how the system reached its conclusion, or only the conclusion. This is the criterion that carries the most weight here, because the FDA–EMA principles ask for interpretability, a clear context of use and human-centric design, and none of those is demonstrable from an opaque output [1][2].
  3. Evidence that it has produced something real. A published clinical readout, a regulatory qualification, a named deployment at scale — as opposed to a platform narrative and a partnership list. This criterion is why Insilico ranks above better-capitalised platforms, and it is applied to EvidenceMD too [7].
  4. Lifecycle coverage from molecule to market. How much of discovery, translational, clinical development, regulatory, safety, medical affairs and commercial the platform actually touches, versus how much it claims. Breadth is worth less than depth, but a single auditable system across several stages is worth more than four disconnected ones.
  5. Fit with GxP and the FDA–EMA governance expectations. Whether the platform supports data provenance documented in line with GxP, risk-based performance assessment, lifecycle monitoring for drift and a human decision-maker — or quietly assumes those away [1][3][4].
  6. Access, time to first value and price transparency. Whether a scientist or a medical affairs lead can use it this week, what it costs, and whether that is published. Half the entries here are enterprise contracts with no public price; two are not purchasable at all.
Ten AI platforms for life sciences and pharma in 2026, ranked in order with no numeric scores, showing the job each one wins, its strongest capability, its main limitation and how it is accessed.
#ToolBest forStrongest atMain limitAccess & pricing
1EvidenceMDClinical and scientific evidence reasoning, from protocol to labelPre-trained and post-trained on peer-reviewed literature; 64k auditable reasoning traceDesigns no molecules, runs no simulations, holds no GxP recordsFree tier worldwide in 30 languages; API from $0.20/request
2Isomorphic LabsStructure-based small-molecule design against difficult targetsAlphaFold 3 lineage; ~$3B in Lilly and Novartis deals; $2.1B Series BNot purchasable; no human safety or efficacy data exists yetPartnership only; first-in-human targeted end of 2026, already slipped once
3Insilico MedicineEnd-to-end generative discovery with a drug actually in Phase 3Rentosertib: Nature Medicine Phase 2a, and Phase 3 dosing since 9 Sep 2026Phase 2a was 71 patients over 12 weeks; no AI drug is approved anywherePharma.AI software licences and co-development deals; HKEX listed
4Veeva AIAgentic work inside the validated systems pharma already runs onAgents across CRM, PromoMats, Safety, Quality, Clinical, Regulatory, MedicalRequires Vault; automates the workflow, does not reason about the scienceUsage-based pricing on top of Veeva Vault; enterprise only
5CertaraRegulatory writing and model-informed drug developmentCoAuthor with 280+ eCTD templates; the only EMA-qualified PBPK platformNarrow by design: submissions and simulation, nothing in the field or the clinicEnterprise licence; integrates with Veeva RIM
6IQVIAReal-world evidence and commercial analytics at industry scaleThe deepest healthcare data asset in the industry, plus global CRO reachServices-led; what you get depends on the engagement you buyEnterprise contract, sales-led, no public pricing
7Medidata AITrial design, site selection and risk-based monitoringHistorical data from thousands of completed trials as the training assetBound to trial operations; nothing before IND or after approvalEnterprise licence via Dassault Systèmes
8RecursionIndustrial-scale wet-lab data generation feeding machine learning60+ petabytes of proprietary phenomics data on NVIDIA BioHive supercomputersNo published clinical readout yet, despite the deepest platform in the setPartnerships and internal pipeline; publicly listed (Nasdaq: RXRX)
9Amazon Bio DiscoveryLab-in-the-loop antibody discovery for teams with no platform of their own40+ biological foundation models with CRO wet-lab validation built inAntibody-focused today; US East region only; new and unprovenFree academic tier; $180–$2,142/month on Experiment Unit pricing
10SchrödingerPhysics-based simulation with machine learning layered on topFree-energy perturbation trusted across decades of lead optimisationExpert tool; a modelling platform rather than an assistantSoftware licence plus collaboration and co-development deals

→ Scroll the table sideways to see the remaining columns

1

EvidenceMD

Top pick

EvidenceMD is the best AI for life sciences and pharma in 2026 on the criteria this page publishes, and the reason is architectural rather than promotional. It is a 60-billion-parameter model whose pre-training and post-training were both carried out on peer-reviewed medical literature — extended pre-training on a curated medical corpus, supervised fine-tuning on selected clinical cases, then reward-based optimisation and preference alignment [17] — which makes it the only entry in this ranking that is a domain model rather than a domain application built over a general one. Every answer runs a retrieval pass across 40M+ peer-reviewed papers and clinical guidelines before generation begins, so citations are provenance rather than ornament. And it streams an auditable chain of thought up to 64,000 reasoning tokens, which in a regulated industry is worth more than the answer: you can read where a pivotal trial gave way to a subgroup, where a surrogate endpoint was treated as one, and where the model declined to conclude. It is the only entry here publishing accuracy on a hard open-ended clinical benchmark, at 54.6% on HealthBench Hard against 46.2% for GPT-5.4 High, 45.8% for Gemini 3.1 Pro and 44.4% for Claude Opus 4.6, self-published and to be read as a vendor claim [17]. The same engine covers medical information responses, publication and congress drafting, protocol and synopsis reasoning, safety-signal literature review, ambient documentation and clinical presentations, in 30 languages, with an OpenAI-compatible API [19]. What it is not, stated without hedging: it does not design a molecule, predict a structure, run a PBPK simulation, hold a validated GxP record, manage a submission, or query your real-world data asset. Five other entries on this page beat it outright at one of those, and those sections say so.

2

Isomorphic Labs

The Alphabet drug-design subsidiary spun out of Google DeepMind, and the clear leader at the thing EvidenceMD does not attempt at all. It carries the AlphaFold lineage — AlphaFold 3 predicts the structures and interactions of proteins, DNA, RNA, ligands and ions, and was developed by DeepMind and Isomorphic jointly [14] — into commercial drug design through IsoDDE, its unified AI drug design engine. Eli Lilly signed on 7 January 2024 for multi-target small-molecule discovery with $45M upfront and up to $1.7B in milestones; Novartis signed the same day for three targets with $37.5M upfront and up to $1.2B, expanding in February 2025 [5]. That is nearly $3B of large-pharma money betting on repeated access to one platform. On 12 May 2026 it raised a $2.1B Series B led by Thrive Capital, with Alphabet, GV, MGX, Temasek, CapitalG and the UK Sovereign AI Fund participating — the deepest capital base in the category by a wide margin. Demis Hassabis framed the round as a shift from proving the science to industrialising it: “Now that we have shown our approach is fundamentally sound, our focus is on scaling our technology to its full potential.” [23] Two honest caveats, and they matter more than the money. No candidate has been named and no human data of any kind exists. And the clinical timeline has already slipped once in public: Hassabis said in 2025 that AI-designed drugs would be in trials by the end of that year, then told an audience at Davos on 20 January 2026 that first trials were now expected by the end of 2026 [6], a position president Max Jaderberg echoed at WIRED Health in April, saying the company was still 'gearing up to go into the clinic' [25]. It is also not a product — you cannot license it, evaluate it or buy it. It ranks second because on molecular design it is the best in the world, and second rather than first because this ranking is weighted for evidence work and because a platform with no human data and no purchase route is a bet rather than a tool.

3

Insilico Medicine

Insilico ranks third — above better-capitalised platforms — on the criterion this page weights third: evidence that something real came out. On 3 June 2025 Nature Medicine published the first randomised Phase 2a of an AI-discovered drug against an AI-discovered target. Rentosertib (formerly ISM001-055), a TNIK inhibitor for idiopathic pulmonary fibrosis, met its primary safety endpoint, and the 60 mg once-daily arm showed a mean forced vital capacity change of +98.4 mL (95% CI 10.9 to 185.9) against −20.3 mL (95% CI −116.1 to 75.6) on placebo [7]. It has since gone further than anyone else in the field: on 9 September 2026 Insilico dosed the first patient in GENESIS-IPF-3, a 52-week, randomised, double-blind, placebo-controlled Phase 3 enrolling roughly 320 patients across 47 centres in China, with annual rate of FVC decline as the primary endpoint — the first Phase 3 anywhere of a generative-AI-discovered drug [24]. Its Pharma.AI stack — PandaOmics for target discovery, Chemistry42 for generative chemistry — is licensable software rather than a closed partnership, which makes it more usable than #2, and the company reports nominating 31 preclinical candidates since 2021 with 13 reaching IND [10]. Read the evidence for exactly what it is: the Phase 2a was 71 patients over 12 weeks, the lung-function result was a secondary endpoint, discontinuations related to liver toxicity and diarrhoea [7], and the Phase 3 has only just begun. Insilico says so itself, in the same release that announced the milestone: “While the initiation of the Phase III trial represents an important milestone in the development of Rentosertib, the drug remains investigational and has not been approved by any regulatory authority.” [24] No AI-discovered drug has been approved by any regulator anywhere.

4

Veeva AI

Veeva wins the system-of-record column outright, and that column is not close. Veeva AI Agents became available for Vault CRM and PromoMats on 3 December 2025 — a Free Text Agent that flags issues in call notes, a Voice Agent, a Pre-call Agent, a Quick Check Agent that screens content against editorial, brand, market, channel and compliance guidelines before MLR, and a Content Agent for review assistance — with a published rollout across Safety and Quality in April 2026, Clinical Operations, Regulatory and Medical in August 2026, and Clinical Data in December 2026 [8][9]. The decisive property is not the models, which are Anthropic and Amazon models hosted on Bedrock [9]; it is that the agents operate inside existing user access controls, permissions and audit trails [8], which is precisely what a validated GxP environment demands and what no standalone reasoning model can offer. Veeva also acquired Copli on 23 June 2026 and launched it as Veeva Falcon MLR, an agentic MLR product aiming to eliminate 70% or more of manual MLR labour within five years [11]. Where it loses to EvidenceMD: these are workflow agents, not scientific reasoners. They check content against a label and a guideline set; they do not read across 40M papers and construct an argument about whether a claim is supportable, and they expose no reasoning trace. If you run Vault, you should have both, and the division of labour is obvious.

5

Certara

Certara ranks fifth on depth rather than breadth, and it beats EvidenceMD outright on regulatory submissions and on quantitative pharmacology. CoAuthor is a regulatory and medical writing product that lives inside Microsoft Word, built on a biomedical GPT with a retrieval-augmented architecture that references only the source data you allow, with more than 280 eCTD templates for patient narratives, clinical study reports, protocols and PK/PD reports, and an integration with Veeva RIM announced on 21 October 2025 so writers can link source files without importing them [12][13]. Separately — and this is a different product from the AI one, which matters — the Simcyp Simulator received an EMA qualification opinion on 4 August 2025, making it the first and only PBPK platform to hold one. The qualification covers three defined contexts of use spanning six CYP enzymes and two inhibition mechanisms, within which a sponsor can use simulation in place of a clinical drug–drug interaction study without re-establishing the platform's credibility from scratch [28]. Version 25, released 5 March 2026, added AI-enabled chat support, with Certara Predictive Technologies president Rob Aspbury describing it as building “on last year's Simcyp Simulator EMA Qualification milestone” to “advance regulatory-accepted PBPK approaches” [14]. That qualification is the strongest single regulatory credential held by anything in this ranking, and two things follow: no reasoning model replaces a qualified PBPK platform, and EvidenceMD does not try — but note also that what EMA qualified is biosimulation, not Certara's generative AI. Vendors in this category routinely let the halo from one product drift onto the other. It ranks fifth because it is deliberately narrow — it does not answer the medical information question, does not work in the field, and its generative layer publishes no accuracy benchmark.

6

IQVIA

IQVIA is ranked here for the asset rather than the algorithm, and it beats EvidenceMD outright on real-world evidence, which is a whole category EvidenceMD does not enter. No reasoning model can tell you what actually happened to 200,000 patients on your comparator in the community, because that question is answered by longitudinal claims, prescription and EMR data — and IQVIA holds one of the largest such assets in existence, wrapped in a CRO and a commercial analytics business that together touch most of the industry. Black Book's 2026–2027 Life Sciences AI Technology Performance Benchmark, published 10 August 2026 on 1,277 verified buyers and users across 254 market participants, names IQVIA among its clinical-development and evidence leaders [15]. The caveat is structural rather than technical: this is a services relationship, so the quality of the AI you experience is largely the quality of the engagement you scoped and the team you were assigned, and almost nothing about it is self-serve, priced publicly or evaluable in an afternoon. For an RWE study it is a first call. For a medical science liaison who needs a defensible answer to a physician's question before Thursday, it is the wrong shape entirely.

7

Medidata AI

Medidata's advantage is the same kind as IQVIA's — a data asset that cannot be reproduced by reasoning — narrowed to one stage. Its platform has carried a very large share of the industry's clinical trials, and the historical patient-level and operational data from those studies is what powers its AI products for protocol design, feasibility, site selection, enrolment forecasting, synthetic control arms and risk-based monitoring. Black Book's 2026–2027 benchmark lists Medidata first among its clinical-development and evidence category leaders [15][16]. Where it wins against EvidenceMD: if the question is which twelve sites will actually enrol this protocol in this indication in these countries, that is an operational prediction from historical trial performance, and no amount of literature reasoning substitutes for it. Where it stops: it is bound to trial execution. It does not answer the scientific question behind the protocol, does not support medical affairs, does not touch post-approval safety or field medical, and exposes no reasoning trace for the recommendations it makes. It ranks seventh because the depth is genuine and the scope is one stage of ten.

8

Recursion

Recursion is the largest industrialised lab-in-the-loop operation in the sector: automated experimentation generating proprietary phenomics data at a scale nobody else matches — more than 60 petabytes — with machine learning trained on it and NVIDIA-built BioHive supercomputing behind it, reinforced by the Exscientia merger completed on 20 November 2024 which added generative chemistry to a business that was previously biology-heavy. On criterion four, lifecycle coverage, it beats most of this list, because it owns the whole loop from experiment to design to experiment. The best single piece of evidence that the loop works is REC-7735, a PI3Kα H1047R inhibitor the company took from first novel hit to development candidate in 10 months and 242 compounds with over 100-fold mutant-over-wild-type selectivity, IND cleared and Phase 1/2 starting in the second half of 2026; Genentech also exercised its first Validated Target Option in neuroscience in Q2 2026 under a Roche collaboration that has paid out $216M in upfront and milestones so far. It ranks eighth rather than higher on criterion three, applied consistently: it has a clinical pipeline — REC-4881 in Phase 2, REC-1245 in Phase 1 — but no published clinical readout of the kind Insilico now has, and a platform company is judged on what has come out of the platform. Like Isomorphic, it is not something a medical affairs or clinical development team can buy and use.

9

Amazon Bio Discovery

Announced on 14 April 2026 at the AWS Life Sciences Symposium, Amazon Bio Discovery is the most accessible thing in this ranking's discovery half, and accessibility is criterion six. It provides 40+ AI biology models with AI-guided selection, agentic assistants that help configure experiments, and direct integration with CRO partners including Twist Bioscience and Ginkgo Bioworks so top candidates are physically synthesised and tested and the results flow back into the next design cycle [18][20]. Pricing is published, which almost nothing else here manages: a free academic tier for verified .edu and .org researchers at 5 Experiment Units a month, then Starter at $180, Pro at $486 and Pro+ at $2,142 per month, with 50% off list through 15 October 2026 [21]. Early adopters named include Memorial Sloan Kettering, Bayer, the Broad Institute and Voyager Therapeutics, and AWS notes that 19 of the top 20 global pharmaceutical companies already run research workloads on its infrastructure [18]. The limits are real: it is scoped to antibody discovery today, it is available only in the US East (N. Virginia) region, and it is months old with no published outcomes. It ranks ninth on track record, and would rank far higher on access alone.

10

Schrödinger

Schrödinger is the oldest argument in computational drug discovery and still one of the best: physics first, machine learning as an accelerant, rather than a model that learned chemistry from data and hopes it generalises. Its free-energy perturbation methods for binding-affinity prediction are the reference standard for lead optimisation in medicinal chemistry, and the company runs both a software business and an internal pipeline, which means it eats its own output. Black Book's 2026–2027 benchmark names it among the discovery and preclinical category leaders [15]. It ranks tenth on this page's criteria and would rank far higher on a chemistry-weighted one — that is the clearest illustration of what the published weighting does, and why you should re-order the list if your job is lead optimisation. What keeps it here: it is an expert modelling platform requiring trained computational chemists, it answers a narrow physical question extremely well, and it has nothing to say about a protocol, a signal, a label or a physician's question.

What do the FDA and EMA now expect from AI in drug development?

On 14 January 2026 the FDA and EMA jointly published ten guiding principles of good AI practice in drug development, covering evidence generation and monitoring across every phase from early research through clinical trials, manufacturing and safety monitoring [1][2]. They are not binding, and they will underpin the guidance that follows in both jurisdictions — which makes them the right lens for choosing a platform now rather than re-choosing one later. Four of the ten decide most procurement questions, and they are the reason this ranking weights auditable reasoning so heavily.

Human-centric by design, with a clear context of use

Principles one and four. The same AI model warrants completely different validation evidence depending on the regulatory question it is being used to answer and how much weight that answer carries — this is the context of use concept at the centre of the FDA's January 2025 draft guidance, which proposes a seven-step, risk-based credibility assessment framework built around it [3]. The practical consequence for buyers: do not procure 'AI for medical affairs'. Procure a defined context of use, decide the risk tier, and require credibility evidence proportionate to it. A platform that shows you its reasoning lets you assess its credibility per use; one that does not forces you to assess it once, globally, and hope. Two precedents show how narrow a real qualification is. Certara's Simcyp is qualified for three specific contexts of use covering six CYP enzymes [28]. Unlearn.AI's PROCOVA received a favourable CHMP qualification opinion in September 2022 — the first time a regulator formally backed a machine-learning method for reducing trial sample size — and even there, what EMA qualified was the statistical method of prognostic covariate adjustment, not digital twins replacing a control arm. CHMP's own language is narrow and worth reading literally: the procedures “could enable increases in power and/or decreases in sample size in phase 2 and 3 clinical trials with continuous outcomes.” [29] The trials stay randomised and keep a real control group.

Interpretability and explainability are named requirements

Principle seven asks that development leverage fit-for-use data considering interpretability, explainability and predictive performance, and that good design promote transparency, reliability, generalisability and robustness for AI contributing to patient safety [1]. Principle ten asks for clear, essential information. This is the clause that turns a visible reasoning trace from a nice-to-have into the cheapest available compliance artefact: EvidenceMD's 64,000-token chain of thought is, in practice, per-answer explainability documentation you did not have to write. The EMA reflection paper reaches the same place from the other direction, warning that models with very large numbers of parameters in non-transparent architectures introduce risks that must be actively mitigated [4].

Data governance and documentation, in line with GxP

Principle six requires that data source provenance, processing steps and analytical decisions be documented in a detailed, traceable and verifiable manner, in line with GxP requirements, with privacy and protection for sensitive data maintained across the lifecycle [1]. Two things follow. Retrieval-bound generation makes provenance a property of the output rather than a reconstruction exercise. And where the work must sit inside a validated environment with preserved permissions and audit trails, that is a genuine argument for doing it inside a system of record — which is exactly Veeva's claim and exactly where it beats a standalone model [8].

Risk-based performance assessment and lifecycle management

Principles eight and nine ask for performance assessment of the complete system including human–AI interaction, using fit-for-use metrics appropriate to the context of use, and for risk-based quality management with scheduled monitoring and periodic re-evaluation to address data drift [1]. Note what principle eight actually says: you are validating the human-plus-model system, not the model. That reframes the buying question. A model that a reviewer can check quickly and correctly produces a better validated system than a marginally more accurate model whose output has to be taken on trust — which is the single strongest argument for transparent reasoning in this entire guide.

When is EvidenceMD not the right choice?

A ranking that never names a loss is advertising. There are five situations where EvidenceMD is the wrong tool for a life sciences or pharma team, and in every one of them something else on this page is the right answer.

You need to design a molecule, predict a structure or optimise a lead

Use Isomorphic Labs, Insilico Medicine, Schrödinger or Amazon Bio Discovery

This is not a gap EvidenceMD intends to close. It does not predict protein structure, generate candidate chemistry, run docking or compute binding free energies. AlphaFold 3 predicts the structures and interactions of proteins, DNA, RNA, ligands and ions [14]; Insilico's Chemistry42 and PandaOmics generate chemistry against discovered targets and have a Phase 2a readout behind them [7]; Schrödinger's free-energy perturbation is the reference for affinity prediction; and Amazon Bio Discovery puts 40+ biological foundation models and CRO wet-lab validation behind a published price list [18][21]. If your job is discovery, the top of this list is not your answer and this guide will not pretend otherwise.

The output has to live inside a validated GxP system with an audit trail

Use Veeva AI inside Vault, or Certara CoAuthor into Veeva RIM

Principle six requires provenance and processing documented traceably in line with GxP [1]. A reasoning model produces an excellent answer that then has to be carried, by a human, into the system where the record actually lives. Veeva AI Agents operate directly inside Veeva application data, documents and workflows, within established user access controls, permissions and audit trails [8], and CoAuthor links source files from Veeva RIM into the document being written [13]. For records management, the system of record wins and it is not a close call.

You need a regulatory-grade quantitative model

Use Certara Simcyp or another qualified platform

A PBPK prediction that supports a dosing recommendation in a submission is a qualified model with a validation package, not an argument from the literature. Simcyp is the first and only EMA-qualified PBPK platform for drug–drug interaction assessment, first-in-human dose prediction, special-population modelling and bioequivalence analysis [14]. EvidenceMD will reason about the pharmacology, the interaction mechanism and what the literature supports, and it will not produce a qualified simulation. Under the FDA's credibility framework the evidentiary burden here is high and specific [3].

The question is about what happened to real patients in the real world

Use IQVIA, Medidata or another real-world data platform

Literature reasoning and real-world evidence answer different questions. 'What does the evidence support for this population' is a reasoning question. 'What is the observed persistence on our comparator in Germany over 24 months' is a data question, and it is answered by a longitudinal asset you either have or do not [15]. EvidenceMD holds no patient data of its own and will not invent an epidemiological estimate — it will tell you what the published literature reports, which is a different and smaller claim.

You need a fully automated decision with no human reviewer

Use a qualified, validated system under documented governance

No entry on this page should be used this way, and EvidenceMD least of all. It is decision support, it is not cleared as a medical device, and it makes no regulatory determination. The FDA–EMA principles are built around human-centric design, risk-based performance assessment of the complete human–AI system, and lifecycle monitoring [1]. Any vendor presenting an AI on this page as removing the reviewer is presenting it wrongly, and that includes this one.

Which tool fits your role?

The right answer depends entirely on which part of the lifecycle you sit in. Six common life sciences roles, and what to actually use.

Medical affairs and field medical (MSL) leads

EvidenceMD first, and this is its strongest single fit. Medical information responses, MSL briefing documents, congress and publication drafting, and standard response letters all require a defensible evidence chain, and a retrieval-first system with a readable reasoning trace turns review from a re-derivation into a check. The 30-language coverage matters for affiliates who currently work from English-only summaries. Pair with Veeva for the record and the MLR path [8][11].

Clinical development and clinical operations

Medidata for operations, EvidenceMD for the science behind them. Site selection, feasibility and enrolment forecasting are predictions from historical trial data, and Medidata's asset is the reason to buy it [15][16]. The endpoint rationale, the comparator justification, the inclusion criteria argument and the literature behind the protocol are reasoning problems — run those through EvidenceMD and keep the 64,000-token trace with the protocol file, because that is your oversight evidence under principle eight [1].

Pharmacovigilance and drug safety

A case-processing platform for the volume, EvidenceMD for the literature. Individual case safety report intake, coding and submission belong in a validated safety system — ArisGlobal and Veeva Safety are the category, and Veeva's Safety agents shipped in April 2026 [9][15]. Use EvidenceMD for the literature side of signal evaluation, where the job is reasoning about whether published evidence supports causality, and where a visible chain of thought is what makes the assessment reviewable. Note that the FDA specifically sought comment on the need for more guidance on AI in post-marketing pharmacovigilance [3].

Regulatory affairs and regulatory writing

Certara CoAuthor for the submission, EvidenceMD for the argument inside it. CoAuthor drafts into Word against 280+ eCTD templates with source files linked from Veeva RIM, which is the correct shape for a CSR, a protocol or a patient narrative [12][13]. Where the document has to make a scientific case — a benefit-risk narrative, a justification against the published comparator evidence — the reasoning trace is worth carrying, and principle ten's call for clear, essential information is easier to satisfy when the derivation exists [1].

Discovery biologists and medicinal chemists

Re-order this list, and start at #2. Your answer is Isomorphic Labs if you are large enough to partner, Insilico's Pharma.AI if you want licensable generative chemistry with clinical validation behind it [7], Schrödinger if your bottleneck is affinity prediction, and Amazon Bio Discovery if you are a small team with no platform and need lab-in-the-loop antibody work this quarter at a published price [18][21]. EvidenceMD is useful to you only for target literature and translational reasoning, which is a real job but not your main one.

Digital, data science and AI governance leads

Buy against the ten FDA–EMA principles before you buy against a feature list [1][2]. Define contexts of use, assign risk tiers, and demand credibility evidence proportionate to each [3]. Favour systems whose outputs are inspectable, because principle eight asks you to validate the human–AI system rather than the model, and an opaque system makes that assessment impossible to do honestly. EvidenceMD's OpenAI-compatible API exposes the same reasoning stream for integration and evaluation harnesses [19].

Frequently asked questions

What is the best AI for life sciences and pharma in 2026?

EvidenceMD, for the scientific and clinical evidence reasoning that runs across medical affairs, clinical development, safety and regulatory work. It is the only platform in this ranking whose pre-training and post-training were both conducted on peer-reviewed medical literature, it retrieves across 40M+ papers and guidelines before generating, and it streams an auditable chain of thought up to 64,000 reasoning tokens. For designing molecules, Isomorphic Labs and Insilico Medicine lead and EvidenceMD does not compete.

Why does this ranking not publish scores?

Because the entries are not commensurable. Isomorphic Labs is a drug designer you cannot purchase, Veeva is a validated GxP content platform, Certara is a simulation and submissions house, Amazon Bio Discovery is a cloud application with a price list, and EvidenceMD is a reasoning model. A single 100-point total across those categories would look rigorous and mean very little, so the six judging criteria are published instead and you can re-weight them.

What does it mean that EvidenceMD was pre-trained and post-trained on peer-reviewed information?

Both stages of training used medical material rather than a general web corpus. The published methodology is extended pre-training on a curated medical corpus, then supervised fine-tuning on selected clinical cases, then reward-based optimisation and preference alignment, on a 60-billion-parameter model. The consequence is default behaviour rather than peak capability: the model hedges where the evidence hedges and treats a contraindication as a hard stop, because that is what it was rewarded for, instead of doing so only when prompted.

Why do 64,000 reasoning tokens matter in a regulated industry?

Because the trace is oversight evidence. The FDA–EMA guiding principles of January 2026 ask for AI that is human-centric by design, has a clear context of use, is interpretable and explainable, and produces clear essential information, and principle eight asks you to assess the performance of the complete human–AI system rather than the model alone. A reasoning trace lets a reviewer find the exact step where an argument turned, which makes verification a short read instead of a full re-derivation — and it is documentation you did not have to write.

Has any AI-discovered drug been approved?

No. As of September 2026 no AI-discovered or AI-designed drug has received regulatory approval anywhere. The most advanced programme is Insilico Medicine's rentosertib, which met its primary safety endpoint in a randomised Phase 2a of 71 patients published in Nature Medicine on 3 June 2025, with a mean FVC change of +98.4 mL in the 60 mg once-daily arm against −20.3 mL on placebo, and which dosed its first Phase 3 patient on 9 September 2026 in GENESIS-IPF-3, a 52-week trial of roughly 320 patients across 47 Chinese centres. That is the first Phase 3 of a generative-AI-discovered drug anywhere, and it has only just started. The Phase 2a lung-function result was a secondary endpoint in a small, short trial: proof of concept, not proof of efficacy.

Is AI better than humans at pharmaceutical and medical work yet?

It depends entirely on which half of the work you mean, and conflating the two is the most common error in this category. On clinical conversation, frontier models have passed unassisted physicians: on OpenAI's HealthBench Professional, ChatGPT for Clinicians scored 59.0 against 43.7 for specialist-matched physicians who had unlimited time and web access, and in a repeat of the original HealthBench physician experiment against April 2025 models, physicians' edits no longer improved on the model's answers. On autonomous scientific execution the picture inverts completely. On BixBench, 296 open-ended real-world bioinformatics questions, Claude 3.5 Sonnet scored 17% and GPT-4o 9%, and in multiple-choice format with an opt-out both performed worse than random because they decline hard questions. On LAB-Bench, every model tested except one performed near chance on figure reasoning. The BixBench authors put their own conclusion plainly: “fully autonomous bioinformatics research remains out of reach (for now!).” Superhuman at the medical conversation, far below expert at running the science — and you should never carry a benchmark from one half across to the other.

Is EvidenceMD better than Isomorphic Labs or Insilico Medicine?

Not at the thing they do, and this guide states that in their sections and in the intro. Isomorphic Labs and Insilico design molecules; EvidenceMD does not predict a structure, generate chemistry or run docking. They are ranked below it only because this page's criteria are weighted toward evidence reasoning, and because neither is a product a medical affairs or clinical development team can buy. On a discovery-weighted ranking they would be first and second.

Can AI be used in an FDA or EMA submission?

Yes, subject to credibility evidence proportionate to the context of use. The FDA's draft guidance of 7 January 2025 proposes a seven-step, risk-based credibility assessment framework covering nonclinical, clinical, post-marketing and manufacturing uses, and it remains a draft. The EMA's reflection paper, adopted in September 2024, covers the full medicinal product lifecycle including discovery. The ten joint FDA–EMA guiding principles published on 14 January 2026 will underpin future guidance in both jurisdictions. Engage early and document the context of use before the model output reaches a submission.

Is EvidenceMD GxP validated or a regulated medical device?

No to both, and that should be read as a limit rather than a caveat. EvidenceMD is clinical and scientific decision support, it is not cleared as a medical device, it makes no regulatory determination, and it is not a validated GxP system of record. Where a record must live inside a validated environment with preserved permissions and audit trails, Veeva Vault is the right destination and EvidenceMD is the reasoning layer feeding it. Its compliance position — HIPAA, a BAA on eligible plans, Azure East US 2 hosting with no non-US region, and SOC 2 Type II in progress rather than complete — is set out in full in the Trust Center [22].

What is the best AI for medical affairs specifically?

EvidenceMD for the evidence work and Veeva for the record. Medical information responses, MSL briefings, congress materials and publication drafting all need an evidence chain a reviewer can verify, which is what retrieval-first generation and a visible reasoning trace provide. Veeva Medical, plus PromoMats and the Falcon MLR product launched on 23 June 2026, is where the content is governed, reviewed and stored. Black Book's 2026–2027 benchmark names Veeva Medical its medical affairs category leader.

What is the best AI for pharmacovigilance and drug safety?

A validated safety platform for case processing, with a reasoning model for the literature side. ArisGlobal and Veeva are the named leaders for safety and regulatory in Black Book's 2026–2027 benchmark, and Veeva's Safety agents shipped in April 2026. Use EvidenceMD where the task is reasoning about whether published evidence supports causality for a signal, because that is a judgement a reviewer has to be able to check. Note that FDA explicitly sought comment on whether more specific guidance is needed for AI in post-marketing pharmacovigilance.

How much do these platforms cost?

Most do not publish a price. EvidenceMD has a free tier worldwide with no licence verification and an OpenAI-compatible API at $0.20 per request for evidencemd-fast and evidencemd-pro and $0.25 for evidencemd-deep. Amazon Bio Discovery publishes tiers: free for verified academic users, then $180, $486 and $2,142 per month, with 50% off list through 15 October 2026. Veeva AI is usage-based on top of a Vault licence. Isomorphic Labs and Recursion cannot be bought at all, and Certara, IQVIA and Medidata are sales-led enterprise contracts.

Can pharma teams use general models like GPT, Claude or Gemini instead?

For drafting, translation, code and restructuring text you supply, yes. For evidence work, the structural problem is that they write from training recall and attach references afterwards, which produces citations that are real and correctly formatted and do not support the claim — the failure mode that costs the most reviewer time in medical affairs and publications. They also publish no domain-specific clinical accuracy figure in most cases. Frontier APIs are ranked separately in our guide to the best APIs for life sciences and pharma, where OpenAI, Google DeepMind and Anthropic all place highly for building on.

The bottom line

EvidenceMD is the best AI for life sciences and pharma in 2026 on the criteria this page publishes: it is the only platform here that is a domain model rather than a domain application, with pre-training and post-training both conducted on peer-reviewed medical literature, retrieval across 40M+ papers and guidelines completing before a word is generated, and an auditable 64,000-token chain of thought that doubles as the interpretability and oversight evidence the FDA and EMA jointly asked for in January 2026 [1][2]. It is also the only entry publishing accuracy on a hard open-ended clinical benchmark, at 54.6% on HealthBench Hard, self-published and to be read as such [17]. What it does not do is large and clearly bounded. It designs no molecules, so Isomorphic Labs, Insilico Medicine, Schrödinger and Amazon Bio Discovery beat it outright there. It holds no validated records, so Veeva wins GxP. It runs no qualified simulation, so Certara's EMA-qualified Simcyp wins quantitative pharmacology. It owns no patient data, so IQVIA and Medidata win real-world evidence and trial operations. The honest recommendation for most pharmaceutical organisations is therefore a stack rather than a winner: a system of record for what must be validated, a data platform for what must be observed, a discovery platform if you make molecules — and one auditable reasoning layer over all of it, with a human reviewer on top of everything, exactly as the ten principles assume [1].

Sources & related evidence

Regulator publications, peer-reviewed results, vendor announcements and independent benchmarks behind this ranking. Competitor capabilities are cited to each vendor's own materials or to the primary literature rather than to our summary of them.

  1. Source 1. Guiding Principles of Good AI Practice in Drug Development (FDA–EMA, January 2026)U.S. Food and Drug Administration, CDER and CBER, with the European Medicines Agency. Ten joint principles published 14 January 2026: human-centric by design, risk-based approach, adherence to standards, clear context of use, multidisciplinary expertise, data governance and documentation in line with GxP, model design and development practices considering interpretability and explainability, risk-based performance assessment of the complete system including human–AI interactions, life cycle management with monitoring for data drift, and clear essential information.
  2. Source 2. EMA and FDA set common principles for AI in medicine developmentEuropean Medicines Agency, 14 January 2026. Announcement of the ten joint principles, their scope across all phases from early research and clinical trials to manufacturing and safety monitoring, and confirmation that they will underpin future AI guidance in both jurisdictions, with EU guideline development already underway.
  3. Source 3. Considerations for the Use of Artificial Intelligence To Support Regulatory Decision-Making for Drug and Biological ProductsU.S. Food and Drug Administration draft guidance, issued 7 January 2025, docket FDA-2024-D-4689. Proposes a seven-step, risk-based credibility assessment framework built around the context of use, covering nonclinical, clinical, post-marketing and manufacturing phases. Comment period closed 7 April 2025; the document remains a draft.
  4. Source 4. Reflection paper on the use of Artificial Intelligence in the medicinal product lifecycleEuropean Medicines Agency, EMA/CHMP/CVMP/83833/2023, adopted September 2024. Covers development, authorisation and post-authorisation, explicitly including drug discovery and nonclinical development, and sets expectations on risk-proportionate validation, bias mitigation, transparency and early regulatory interaction.
  5. Source 5. Isomorphic Labs collaborations with Eli Lilly and NovartisIsomorphic Labs, 7 January 2024. Multi-target small-molecule research collaborations with Eli Lilly ($45M upfront, up to $1.7B in milestones) and Novartis ($37.5M upfront, up to $1.2B), together worth nearly $3B excluding royalties.
  6. Source 6. AI-designed drugs by a DeepMind spinoff are headed to human trialsWIRED, reporting Isomorphic Labs president Max Jaderberg at WIRED Health in London on 16 April 2026 saying the company is 'gearing up to go into the clinic', later than the end-2025 target previously stated by CEO Demis Hassabis, with an oncology and immunology pipeline and a $600M raise behind the clinical build-out.
  7. Source 7. A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trialNature Medicine, 3 June 2025 (DOI 10.1038/s41591-025-03743-2, ClinicalTrials.gov NCT05938920). Randomised, double-blind, placebo-controlled Phase 2a of rentosertib (formerly ISM001-055) in 71 patients over 12 weeks. Primary safety endpoint met with comparable treatment-emergent adverse event rates; mean forced vital capacity change +98.4 mL (95% CI 10.9 to 185.9) in the 60 mg once-daily arm against −20.3 mL (95% CI −116.1 to 75.6) on placebo; discontinuations most commonly related to liver toxicity or diarrhoea.
  8. Source 8. Veeva AI Agents Now AvailableVeeva Systems, 3 December 2025. Availability of AI Agents for Vault CRM (Free Text, Voice, Pre-call) and PromoMats (Quick Check, Content), operating within established user access controls, permissions and audit trails, with additional agents for clinical, regulatory, safety, quality, medical and commercial planned through 2026.
  9. Source 9. Veeva AI Agents to Be Released Across All Veeva ApplicationsVeeva Systems, 14 October 2025. Published rollout schedule: Vault CRM and PromoMats December 2025, Safety and Quality April 2026, Clinical Operations, Regulatory and Medical August 2026, Clinical Data December 2026. Agents use Anthropic and Amazon models hosted on Amazon Bedrock, with custom agents on Veeva-hosted, Bedrock or Azure AI Foundry models, under usage-based pricing.
  10. Source 10. Insilico completes first-in-human dosing of ISM8969Insilico Medicine (HKEX: 3696). First-in-human dosing in the Phase 1 study of ISM8969, an orally available brain-penetrant NLRP3 inflammasome inhibitor, under a co-development collaboration with Hygtia Therapeutics. Also states 31 preclinical candidates nominated since 2021, 13 with IND approval or filing, at an average of 12 to 18 months to candidate nomination.
  11. Source 11. Veeva acquires Copli and launches Veeva Falcon MLRVeeva Systems, 23 June 2026. Acquisition of Copli and launch as Veeva Falcon MLR, an agentic medical, legal and regulatory review product running compliance checks against approved labels and local regulations, with a stated aim of eliminating 70% or more of manual MLR labour within five years.
  12. Source 12. Certara CoAuthorCertara. Generative AI regulatory and medical writing software integrated into Microsoft Word, built on a biomedical GPT with a proprietary retrieval-augmented generation architecture that references only permitted source data, with more than 280 eCTD templates for patient narratives, clinical study reports, protocols and PK/PD reports, and ISO 27001 certification for Certara's information security management system.
  13. Source 13. Certara joins the Veeva AI Partner ProgramCertara, 21 October 2025. Integration between CoAuthor and Veeva RIM allowing regulatory writers to link source files directly from Veeva RIM without importing them into CoAuthor.
  14. Source 14. Certara expands the Simcyp Simulator (Version 25)Certara, 5 March 2026. Release of Simcyp Simulator Version 25, described as the first and only EMA-qualified physiologically based pharmacokinetic platform, supporting drug–drug interaction assessment, first-in-human dose prediction, special population modelling and formulation and bioequivalence analysis, with AI-enabled chat support added in this version.
  15. Source 15. 2026–2027 Life Sciences AI Technology Performance BenchmarkBlack Book Research with Applied Artificial Intelligence LLC, 10 August 2026. Independent client-rated benchmark across 28 categories and 254 market participants based on 1,277 verified buyers and users, naming Insilico Medicine, Schrödinger, Generate:Biomedicines, Tempus AI, Clarivate Cortellis and Certara among discovery and preclinical leaders; Medidata, Tempus AI/Deep 6 AI, CluePoints, ActiGraph, Veradigm, Datavant/Aetion and IQVIA in clinical development and evidence; and ArisGlobal, Veeva, InterSystems, DNAnexus, PathAI and Siemens/Dotmatics in safety, regulatory and data.
  16. Source 16. Medidata AIMedidata, a Dassault Systèmes company. Product documentation for AI applications built on historical clinical trial data, covering trial design and feasibility, site selection, enrolment forecasting, synthetic control arms and risk-based monitoring.
  17. Source 17. EvidenceMD benchmark results and model methodologyEvidenceMD. Published benchmark methodology and results for the 60-billion-parameter clinical reasoning model, including the training pipeline described in this guide — extended pre-training on a curated medical corpus, supervised fine-tuning on selected clinical cases, reward-based optimisation and preference alignment — the 64,000-token reasoning architecture, retrieval binding over 40M+ peer-reviewed papers and guidelines, and 54.6% on HealthBench Hard against 46.2% for GPT-5.4 High, 45.8% for Gemini 3.1 Pro and 44.4% for Claude Opus 4.6. Self-published by the vendor and to be read as a vendor claim.
  18. Source 18. Introducing Amazon Bio DiscoveryAmazon Web Services, April 2026. Agentic lab-in-the-loop antibody discovery application with 40+ AI biology models and AI-guided model selection, agentic assistants for experiment configuration, and integrated CRO partners including Twist Bioscience and Ginkgo Bioworks, with A-Alpha Bio anticipated. States that 19 of the top 20 global pharmaceutical companies use AWS for research workloads, and that customers retain ownership of proprietary data, models and resulting intellectual property.
  19. Source 19. EvidenceMD developer platformEvidenceMD. OpenAI-compatible clinical reasoning API exposing the same streamed reasoning trace and inline peer-reviewed citations used in the product, across evidencemd-fast, evidencemd-pro and evidencemd-deep, with 30-language support.
  20. Source 20. Closing the loop with Amazon Bio Discovery's integrated lab partnersAmazon Web Services. Detail on the CRO launch partners that synthesise and test top candidates and route wet-lab results back into the next design cycle, and on the data isolation and intellectual property position for proprietary models and results.
  21. Source 21. Amazon Bio Discovery pricingAmazon Web Services. Published tiers based on monthly Experiment Units: Academic free for verified .edu and .org researchers at 5 EUs/month, Starter $180/month, Pro $486/month, Pro+ $2,142/month and Enterprise by contract, with 50% off list price for new users through 15 October 2026. Available in the US East (N. Virginia) region.
  22. Source 22. EvidenceMD Trust CenterEvidenceMD. Data handling, retention, hosting and compliance position, including HIPAA posture, business associate agreement availability on eligible plans, Azure East US 2 hosting and the current status of SOC 2 Type II certification.
  23. Source 23. Isomorphic Labs secures $2.1 billion to scale its AI drug design engineIsomorphic Labs, 12 May 2026. Series B of $2.1 billion led by Thrive Capital, with existing backers Alphabet and GV alongside new investors MGX, Temasek, CapitalG and the UK Sovereign AI Fund. Capital earmarked for development and deployment of the IsoDDE drug design engine and for advancing the therapeutic pipeline towards the clinic. Follows a $600M first external round in 2025, also led by Thrive.
  24. Source 24. Insilico Medicine doses first patient in GENESIS-IPF-3, the world's first Phase III trial of a generative AI-driven drugInsilico Medicine (HKEX: 3696), 9 September 2026. First patient dosed with rentosertib (ISM001-055 / INS018_055) at Peking Union Medical College Hospital, with Shanghai Pulmonary Hospital enrolling the same day. NCT07687459 / CTR20262475: a 52-week, prospective, randomised, multicentre, double-blind, placebo-controlled, parallel-group Phase III expected to enrol 320 patients across 47 centres in China. Primary endpoint is the annual rate of decline in forced vital capacity over 52 weeks; key secondary endpoint is time to first disease-progression event. The company states that rentosertib remains investigational and has not been approved by any regulatory authority.
  25. Source 25. AI-designed drugs by a DeepMind spinoff are headed to human trialsWIRED, reporting Isomorphic Labs president Max Jaderberg at WIRED Health in London on 16 April 2026 saying the company is 'gearing up to go into the clinic', with an oncology and immunology pipeline behind the clinical build-out.
  26. Source 26. HealthBench ProfessionalOpenAI. Physician-authored benchmark of real clinician tasks across care consult, writing and documentation, and medical research, with rubrics adjudicated by three or more physicians across three phases and roughly one third of examples adversarial by design. ChatGPT for Clinicians scored 59.0 against 43.7 for specialist-matched physician responses written with unbounded time and web access (p = 3.7 × 10⁻¹⁰); base GPT-5.4 scored 48.1. The earlier HealthBench paper found that physicians could improve on September 2024 model responses but no longer improved on April 2025 model responses.
  27. Source 27. BixBench: a comprehensive benchmark for LLM-based agents in computational biologyFutureHouse, March 2025. 296 open-ended real-world bioinformatics and data-analysis questions built on reproducible analysis notebooks. Claude 3.5 Sonnet scored 17% and GPT-4o 9% on open-answer format; in multiple-choice with an opt-out option both performed worse than random because the models decline complex questions, and only marginally above random without the opt-out. The authors conclude that fully autonomous bioinformatics research remains out of reach. Companion benchmark LAB-Bench (2,457 questions across eight categories) found all models substantially underperform humans, with every model except Claude 3.5 Sonnet performing near chance on figure reasoning.
  28. Source 28. Certara Simcyp Simulator becomes first and only platform to receive an EMA qualification opinion for PBPK modelingCertara, 4 August 2025. EMA qualification opinion for the Simcyp Simulator covering three contexts of use spanning six CYP enzymes and two inhibition mechanisms, within which simulation may replace clinical drug–drug interaction studies without sponsors re-establishing platform credibility. The qualification applies to physiologically based pharmacokinetic biosimulation, not to Certara's generative AI products.
  29. Source 29. EMA qualifies Unlearn.AI's PROCOVA method for smaller, faster clinical trialsUnlearn.AI, 28 September 2022. Favourable CHMP qualification opinion for Prognostic Covariate Adjustment (PROCOVA), the first regulatory endorsement of a machine-learning method for reducing sample size in pivotal trials. CHMP concluded the procedures could enable increases in power and/or decreases in sample size in Phase 2 and 3 trials with continuous outcomes. What was qualified is the statistical methodology — training a prognostic model on independent historical data, computing a per-subject prognostic score from baseline data, and using it as an ANCOVA covariate — not the replacement of a randomised control arm.

About EvidenceMD

EvidenceMD is a 60-billion-parameter clinical reasoning model for healthcare and life sciences professionals. Its pre-training and post-training were both conducted on peer-reviewed medical literature rather than a general corpus, generation is bound to retrieval across 40M+ peer-reviewed papers and clinical guidelines, and it streams an auditable chain of thought up to 64,000 reasoning tokens so a reviewer can check a derivation rather than accept a conclusion. It is decision support, not a regulated medical device and not a validated GxP system of record, and it does not replace human review. The Trust Center sets out the full compliance position, and the OpenAI-compatible API exposes the same reasoning stream to developers.

Related reading

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Put a reasoning trace behind your next evidence question

Ask the medical information, protocol or signal question you would normally send to three people, and read the 64,000-token reasoning trace before you accept the answer. Free to start, no licence verification, 30 languages.

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Best AI for Life Sciences and Pharma 2026: 10 Ranked