Large Language Models for Pharma

Large Language Models (LLMs) are reshaping the pharmaceutical and life sciences industry at every stage of the drug development value chain — from target identification and molecular design through clinical operations, regulatory submission, and post-market surveillance. An industry historically defined by 10–15 year development cycles and billion-dollar failure rates is finding that LLMs compress timelines, reduce document burden, and surface biological signal that would otherwise remain buried in literature.

Accelerating Drug Discovery and Target Identification

The foundational challenge in drug discovery is understanding which biological targets are tractable and which chemical matter might interact with them. LLMs trained on PubMed, patent databases, genomic datasets, and proprietary assay results can synthesize decades of literature in seconds. Companies like Recursion Pharmaceuticals and BenevolentAI use LLM-based pipelines to generate hypotheses connecting disease mechanisms to candidate compounds — a process that once took medicinal chemistry teams months of manual review. In 2025, Insilico Medicine advanced its INS018_055 for idiopathic pulmonary fibrosis — a compound identified almost entirely through AI — into Phase II trials, marking one of the first LLM-augmented molecules to reach clinical testing. Protein language models such as ESM-2 (Meta) have enabled zero-shot prediction of protein function from sequence alone, while multimodal systems now jointly reason over molecular graphs, clinical data, and natural language simultaneously.

Clinical Trial Design and Operations

Clinical development consumes roughly 60% of total drug development cost, and much of that cost is administrative and operational. LLMs are being deployed across protocol authoring (Pfizer and Roche have both piloted GPT-4 and Claude-based tools for generating first-draft protocols), patient eligibility screening (parsing unstructured EHR notes against inclusion/exclusion criteria), and site feasibility analysis. Medidata and Veeva have embedded LLM assistants into their clinical trial management platforms that can answer operational queries, flag protocol deviations, and summarize patient narratives. Tempus AI has built LLM pipelines that convert clinical notes into structured trial-matching features, increasing enrollment velocity for oncology studies. The long-context capabilities of 2026-era models — processing 200k tokens in a single pass — are particularly valuable here: an entire patient chart, a full protocol, or a complete adverse event dossier can be analyzed at once rather than chunked and reassembled.

Regulatory Writing and Submission Management

Regulatory submissions — NDAs, BLAs, IND packages, CTD modules — represent some of the most structured and high-stakes document work in any industry. A single NDA can run to 100,000 pages. LLMs now handle first-pass drafting of clinical study reports, non-clinical summaries, and risk management plans, with human experts reviewing and approving rather than authoring from scratch. Certara has commercialized Certara.AI, a suite of LLM tools specifically trained on FDA and EMA guidance documents and historical submission language. Johnson & Johnson and AstraZeneca have both disclosed internal programs using LLMs to cross-reference draft submissions against agency guidelines, flagging inconsistencies before filing. The regulatory intelligence use case — tracking evolving global guidance across FDA, EMA, PMDA, and NMPA simultaneously — is now almost entirely automated at leading pharma companies.

Pharmacovigilance and Safety Signal Detection

Post-market safety monitoring requires processing millions of spontaneous adverse event reports, medical literature, social media signals, and electronic health records. LLMs have transformed this function by automating case narrative coding, duplicate detection, and causality assessment. The FDA's MedWatch database receives over one million reports annually; LLM-powered triage systems at companies including Pfizer, Novartis, and contract research organizations like ICON and Syneos Health now classify and route the majority of incoming cases without human first-pass review. More significantly, LLMs can identify weak safety signals by synthesizing patterns across structured FAERS data, published case reports, and forum posts — signals that individual case reviewers would never surface. In the European context, EudraVigilance signal management has been piloted with LLM augmentation by several large pharma companies in 2025.

Scientific Literature Intelligence and Knowledge Management

Pharmaceutical R&D organizations maintain vast internal knowledge repositories — lab notebooks, preclinical reports, competitive intelligence files, manufacturing deviation records — most of which are unstructured and effectively dark. Retrieval-augmented generation (RAG) architectures built on enterprise LLMs like Claude for Enterprise or GPT-4o allow scientists to query this corpus conversationally. Eli Lilly deployed an internal scientific knowledge assistant in 2024 that indexes over 20 years of internal research documents, enabling scientists to surface relevant prior work that would otherwise require weeks of manual search. Novo Nordisk and Sanofi have similar programs. The compounding effect is significant: when a medicinal chemist can instantly interrogate the full history of a target or scaffold, the iteration cycle compresses dramatically.