Benchmarks for Natural Language Data Mining with LLMs
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Data FAIRy Bioassay Annotation
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Journal Article
Many AI projects in drug development succeed technically but never deliver lasting business value. Why? This peer-reviewed paper, built on multi-year Pistoia Alliance studies, examines what really separates AI initiatives that scale from those that stall. The answer is less about technology than you think.
A peer-reviewed publication by the Pistoia Alliance Social Media Listening and Real-World Evidence project has been recognized in a compilation of highly relevant voices on regulatory science and strategies to address uncertainties in medicines development, evaluation and use.
This peer-reviewed paper, produced by the Pistoia Alliance's Large Language Models in Life Sciences project and published in Drug Discovery Today (May 2026), reports the outcomes of a systematic assessment of current practices in natural language querying with LLMs.
This landmark paper proposes standardized guidelines for Social Media Listening (SML) to generate real-world evidence from patient conversations, capturing authentic insights on disease burden, treatment experiences, and unmet needs, a key for patient-focused drug development.
Instrument, chromatographic data system, and vendor-agnostic digital transfer of machine-readable high-performance liquid chromatography-ultraviolet methods using the allotrope data format
This article highlights how clinical trial site selection is slowed by manual, inconsistent data collection and describes the Pistoia Alliance’s ClinOps Ontology Project, which uses standardized, machine-readable data models to streamline site selection, cut timelines by up to 80%, and improve trial efficiency.
A synthesis of considerations from patients, industry and regulators
Whilst life science companies have come to recognize data as their greatest asset, it is also their greatest challenge. Bioassay protocols are one such example where legacy data management systems are holding R&D back, and where adopting the FAIR (Findable, Accessible, Interoperable, Reusable) principles would improve the usability of the data.
We report results of the systematic business analysis of the personas in the modern pharmaceutical discovery enterprise in relation to their work with the AI and ML technologies. We identify 22 common business problems that individuals in these roles face when they encounter AI and ML technologies at work, and describe best practices (Good Machine Learning Practices) that address these issues.
The Pistoia Alliance gathered a number of UK MRCs focused on complex lifelong conditions. The group used workshops and an opinion questionnaire for a snapshot of how the charities believe their knowledge and patient experiences could contribute insights and efficiencies to commercial R&D.
The UDM (Unified Data Model) is an open, extendable and freely available data format for the exchange of experimental information about compound synthesis and testing. The past, present, and future of the UDM exchange format are discussed in this article and factors that contribute to the successful adoption of the UDM format.
Despite considerable progress, the transformative shift from an application-centric to a data-centric perspective, enabled by FAIR implementation, remains very much a work in progress on the ‘FAIR journey’. In this review, we consider use cases for FAIR implementation.
