Drug Discovery
14 gains2 Traces8 problems
Every measured gain and problem on the record for this topic, newest first. Updated .
One square per record, darker for stronger evidence:Evidence: 0 high15 moderate9 emerging
AI in drug discovery
Most evidence on AI in drug discovery comes from lab benchmarks, cell assays and animal studies, with very little from patients. TRV-2026-0893TRV-2026-0940TRV-2026-2408 Authors of a Nature Reviews Drug Discovery Perspective say that after a decade, evidence of safer, more effective medicines reaching patients faster is disappointingly limited. TRV-2026-0733
A randomized, placebo-controlled trial in 51 healthy adults shows the AI-optimized COVID antibody GB-0669 caused no dose-limiting toxicities and had a 54-day half-life. TRV-2026-0225 In a controlled comparison, 85% of 100 AI-optimized antibiotic peptides were hits, and they outperformed their templates in two mouse models of Acinetobacter baumannii infection. TRV-2026-0193 On trial paperwork, the TrialScout tool linked registrations to published results with 92.5% sensitivity, and human error explained 123 of 200 reviewed disagreements with earlier matches. TRV-2026-0918
An AI literature screener scored an F1 of 0.684 on drug-drug queries, against 0.872 for drug-family queries, in a test on 7,751 articles. TRV-2026-1610 A GPT-4.1 tool reading consent forms matched experts on 92.1% of forms after tuning, which leaves a share of answers wrong. The authors note that hallucinations limit reliability in high-stakes settings. TRV-2026-1539 An AI tool that masks patient identifiers reached 88.48% average accuracy, which means some trial documents were not correctly desensitized. TRV-2026-1530 A review in the International Journal of Pharmaceutics says no AI-designed polymeric-lipid nanoparticle for breast cancer has entered a registered clinical trial. TRV-2026-1298
The anticancer prediction model was validated externally on only 10 FDA-approved compounds, and its ranked predictions for 1200 drugs are unconfirmed. TRV-2026-1723 TrialTriage matched the answer key on all 90 synthetic cases, but the authors say real-world performance is not shown. TRV-2026-0666 The AI-designed bird flu vaccine protected mice and ferrets in an animal study, and it still has to be tested in human clinical trials. TRV-2026-2408 An AI framework flagged 817 terminated trials that may have failed from unwanted drug spread in the body, though no drug has been shown revived. TRV-2026-2336
The strongest results are a small human safety trial of an AI-optimized antibody and animal studies of peptides, a vaccine and prostate cancer drugs. TRV-2026-0225TRV-2026-0193TRV-2026-2408TRV-2026-1644
See these 5 recordsModels rank well on benchmarks and turn up lab hits, but the work is preclinical and some methods are not built for large screens. TRV-2026-0940TRV-2026-1348TRV-2026-1663TRV-2026-1723TRV-2026-1954
See these 4 recordsLab tests plus machine learning flagged liver-toxic drugs in a 170-drug test, while reviews say black-box toxicity models struggle to win regulators' trust. TRV-2026-1295
A Trace is one record with both a gain and a problem, so it counts on both sides: 16 records show a gain and 10 show a problem. 20 context records are in the record list but not counted, because they report no measured gain or problem.
| Evidence | Gain side | Problem side |
|---|---|---|
| HighStrong study design, backed by at least two independent sources | 0 | 0 |
| ModerateA measured study or an official source | 12 | 3 |
| EmergingEarly, small, unreviewed or reported second-hand | 4 | 7 |
By type of evidence: 9 benchmark or lab tests; 6 controlled comparisons; 6 commentaries or narrative reviews; 1 news report; 1 investigation by reporters; 1 randomized trial. How evidence is scored
Against earlier human-coded matches, the TrialScout AI tool linked trial registrations to published results with 92.5% sensitivity and 81.2% specificity, and human error explained 123 of 200 reviewed disagreements.
In a check of 9,000 randomly chosen samples from 100,000 processed trial image records, an AI tool masked patient identifiers with 88.48% average accuracy, reaching 99.40% on serious adverse event documents.
In a placebo-controlled trial of 51 healthy adults, the AI-optimized antibody GB-0669 caused no dose-limiting toxicities, had a 54-day half-life, and showed dose-dependent virus neutralization in blood samples.
In a test on 7,751 articles, the AI literature screener's F1-score was 0.684 for drug-drug queries and 0.731 for single-drug queries, against 0.872 for drug-family queries.
With her liver failing, her skin was a ghostly apricot. The thoughts creep in as we are bombarded by artificial intelligence chief executives making bold proclamations such as “AI will cure cancer in five to 10 years” or “AI will cure cancer in our lifetimes”.
AI tools for matching results, screening patients, reading consent forms and masking identifiers do well in tests but still make errors. TRV-2026-0918TRV-2026-1530TRV-2026-1539TRV-2026-0666TRV-2026-2336
See these 4 recordsReviews and reporting say data problems, weak interpretability and few clinical trials leave the claim of better medicines largely unproven. TRV-2026-1172TRV-2026-0733TRV-2026-0893TRV-2026-1151TRV-2026-1298TRV-2026-0356
See these 5 recordsEach record sits in one thread at most. A record with both a gain and a problem counts on both sides. 3 records fit none of these threads.
The review says poor data quality, limited interpretability and poor generalizability still limit reproducible nanoparticle synthesis and clinical adoption of AI-driven nanomedicine.
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"Medical Laboratory" by ben.dracup, CC BY 2.0.Researchers built a platform in which a large language model turns the free-text eligibility criteria of cancer trials into SQL code, so matching can run as explicit rules without further AI inference.
JNCI Cancer Spectrum
Researchers built DeepFusion-ACSM, an ensemble that combines machine learning and deep learning models to predict whether small molecules have anticancer activity.
Researchers at the University of Cambridge used AI to design a single vaccine aimed at many bird flu variants. In mice and ferrets, it completely protected against a range of genetically distinct viruses that have all…
Researchers built a framework on OpenAI models that reads terminated trial records on ClinicalTrials.gov. It extracts structured information, infers disease sites, and checks whether adverse effects appeared in different…
Researchers built the Virtual Biotech, a group of AI agents organized like a drug-development company, with divisions for target discovery, safety assessment, modality selection, and clinical development.
Researchers built a multi-stage computational pipeline for Parkinson's disease drug discovery. Graph attention networks were used in QSAR modelling on 1393 compounds, followed by docking, density…
Researchers introduced qcGEM, a graph-based molecular embedding that builds quantum chemistry information into AI models for drug discovery.
Researchers benchmarked 10 publicly released quantitative prediction tools for antimicrobial peptides, covering both classical machine learning and deep learning.
A bibliometric review of Web of Science records from 2010 to November 9, 2025 found 9671 publications on AI in pharmacy and pharmaceutical sciences.
This review abstract describes how machine learning and AI are being applied to gastro-retentive drug delivery systems, which keep oral dosage forms in the stomach longer so drugs with narrow…
Researchers assembled a dataset that merged existing fluorescence data with NIR-II fluorophore data, spanning emission wavelengths from visible light to the NIR-II window.
Researchers exposed androgen receptor-negative cells to a library of experimental degraders and measured broad proteome changes.
Researchers built a pipeline that reads biomedical literature to find drug interaction content. A BERT-based sentence classifier flags safety and interaction text, and a named entity recognition model…
AstraZeneca and insitro have formed a collaboration aimed at finding new treatment options for difficult liver and heart conditions.
Researchers tested whether GPT-4.1 combined with retrieval-augmented generation could review how clinical trial informed consent forms describe reuse of participant data.
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