How Pharmaceutical Companies Use AI Tools: Drug Discovery to HEOR

Why pharmaceutical companies use AI tools across the drug development pipeline

Drug development fails far more often than it succeeds, and failure drives the cost. Only about 10% of clinical programmes reach approval (PMID 38632401). Across 406,038 trial entries for 21,143 compounds, oncology showed a 3.4% success rate from Phase I to approval (PMID 29394327). The median capitalised R&D investment per new drug approved by the FDA between 2009 and 2018 was $985.3 million, and the mean was $1.34 billion (PMID 32125404).

AI tools are aimed at the earliest and most expensive decisions: which target, which molecule, which indication. The first analysis of AI-native biotech pipelines found an 80–90% Phase I success rate for AI-discovered molecules, but about 40% in Phase II, comparable to historic industry averages on a small sample (PMID 38692505). The data suggest AI helps produce molecules with drug-like properties; they do not yet show that AI picks better targets.

The practical change in 2026 is integration. Pharma-relevant databases and models are listed as connectors in the Claude connector directory, so a scientist can move from literature to bioactivity to patent records in one session [24]. The sections below map those tools by function, based on their public directory listings as checked on 9 October 2026, and state what each one cannot do.

AI tools for drug discovery: ChEMBL bioactivity, structure prediction and ADMET models

ChEMBL is the measured-data anchor. It is a manually curated open resource of bioactive molecules, and it now holds slightly more deposited bioactivity data than data extracted from literature (PMID 37933841). Its connector needs no sign-in, and its six tools are lookups: compound, drug and target search, bioactivity, mechanism and ADMET [10]. Because it returns stored values rather than predictions, a novel or unpublished series has nothing to retrieve.

Boltz API predicts molecular structures and binding, screens small-molecule and protein libraries against a target, and designs binders. Each workflow estimates cost before it runs, and separate start, status and results tools mean a prediction is collected after the job completes rather than in the same step [11]. Inductive Bio exposes two tools, one to list models and one to predict properties. Its directory listing offers LogD and pKa models, with assays such as microsomal stability, CYP inhibition and hERG available by contacting the company [12].

Prediction confidence is not experimental evidence. When AlphaFold predictions were compared directly with crystallographic maps, even very high-confidence predictions sometimes differed globally in domain orientation and locally in backbone and side-chain conformation (PMID 38036854). The authors recommend treating predictions as hypotheses. The same discipline applies to predicted binding and predicted ADMET.

One gap remains: a directory search on 9 October 2026 returned no connector for the AlphaFold Protein Structure Database, PDB or UniProt [24]. Boltz predicts structures; it does not look up solved ones [11]. A structure-database lookup still needs a custom MCP server.

AI for drug target validation: human genetics, patient cohorts and generated gene expression

Human genetic support is one of the best-documented predictors of target success. Drug mechanisms with genetic support have a 2.6-fold higher probability of success than those without, and the effect rises with confidence in the causal gene (PMID 38632401). Three connectors address this layer, and each returns a different kind of evidence.

Helix GenoSphere returns only de-identified aggregate statistics, such as gene-level carrier counts and patient counts, and suppresses small groups; it gives no access to individual records [13]. Its tools include check_gene_availability, which should run before a null result is read as absence of signal [13]. Owkin powers HistoPLUS, which turns H&E slides from TCGA into searchable histology data and runs cohort-level survival analysis [14]; its results describe those TCGA cohorts. Synthesize Bio generates bulk and single-cell expression profiles from a model of a virtual human rather than measuring samples [15]. That output is useful for shaping hypotheses and should be labelled as generated wherever it appears.

Pharma competitive intelligence and patent AI tools: clinical trial registries and prior art

Before committing to a target, a pharma team asks two questions: is it already claimed, and is anyone already running it in the clinic. Trial registries and patent indexes answer them, with known blind spots.

The Clinical Trials connector needs no sign-in and gives access to ClinicalTrials.gov [16]. Registry records show what sponsors filed, not what happened. Of 4,209 trials legally due to report results, only 40.9% did so within the one-year deadline, and the median delay was 424 days (PMID 31958402).

Amass TrialCore lists 1.2 million+ trials from ClinicalTrials.gov and international registries including ChiCTR, CTRI, EU-CTR, JPRN and IRCT, linked to drugs, diseases, publications and regulatory outcomes. Amass PatentCore, marked Preview, covers 16 million+ life-science patents with full-text search, CPC and IPC classes, families, assignees, inventors and chemical compounds [17]. Patlytics offers semantic prior-art search, full claims and non-patent literature through five read-only tools [18]. Solve Intelligence adds patent drafting support and search across patent law, case law and technical standards [19].

AI tools for pharma Medical Affairs, HEOR and oncology: where clinical coverage runs out

Medical Affairs, HEOR and HTA teams have the thinnest connector coverage in pharma. Cortellis CMC Intelligence exposes a single agent tool over Clarivate's curated regulatory CMC content, covering pre- and post-approval requirements for small molecules and biologics [20]. That scope is narrower than an HTA evidence requirement. Medidata covers platform documentation and predictive trial-site ranking [21], so it supports trial operations rather than literature review. A directory search for HTA, appraisal and reimbursement terms returned no relevant connector [24], so that evidence still arrives by upload.

Clinical oncology has a sharper gap. A directory search for ClinVar, COSMIC, OncoKB and CIViC, the knowledge bases a molecular tumour board relies on for variant tiering, returned no matching connector [24]. Helix gives aggregate population statistics [13], which inform interpretation but do not classify a variant. Any deployment that touches patient data also carries data-handling obligations that the user's organisation, not the connector, has to meet.

Biomedical literature verification: why pharma AI stacks need citation grounding

Every tool above produces structured output: values, structures, records, frequencies. The claims that tie them into an argument, such as "this target is genetically validated" or "this mechanism explains the toxicity", still come from the literature. That is where general-purpose language models are weakest.

In 115 references generated by ChatGPT-3.5 for medical articles, 47% were fabricated, 46% were real but inaccurate, and 7% were real and accurate; an incorrect PMID appeared in 93% of papers (PMID 37337480). Retrieval reduces this. In a 2026 benchmark, non-retrieval models produced non-existent titles at high rates, while the retrieval-augmented OpenScholar-8B produced none (PMID 41639446). Retrieval alone does not check whether a real paper supports the sentence it is cited for.

BioSkepsis covers that step: it searches 40M+ curated biomedical papers and returns a cited, evidence-backed brief [25]. It is also available inside Claude as a connector [26]. It has limits too. Its database is updated weekly [25], so papers from the last few days may be missing, and it produces citation-grounded text, not structures, sequences or predictions. Output from Boltz, Helix or a patent server is outside what it can verify.

Open vs licensed pharma AI stacks: choosing tools by drug development team

Pharma AI stacks fall into two tiers. The open stack uses connectors that need no sign-in: PubMed, ChEMBL and Clinical Trials [10] [16] [22]. It suits pilots and evaluations. The licensed stack uses connectors that require sign-in to an existing vendor account [24].

Workflows built for the licensed stack should degrade rather than fail when a connector is missing. Otherwise every workflow has to be maintained twice.

FAQ: AI tools in pharmaceutical companies