7 Best LLMs Transforming the Pharmaceutical Industry in 2025

Several large language models (LLMs) are already being used or evaluated by pharmaceutical companies and health tech teams. From accelerating drug discovery to streamlining regulatory submissions and enhancing patient education, LLMs are redefining how life sciences teams work with data, documents, and decisions.

Unlike traditional AI systems, LLMs can read, understand, and generate natural language at scale, making them ideal for navigating the complexity of biomedical literature, clinical protocols, compliance guidelines, and real-world evidence.

This article explores some of the most impactful LLMs being used in the pharmaceutical industry today, how they’re being applied across R&D, clinical, regulatory, and patient-facing workflows, and what it means for the future of AI in life sciences.

Here are some LLMS details:

1. BioGPT (Microsoft)

Use in pharma:
BioGPT is a domain-specific LLM developed by Microsoft, trained exclusively on over 15 million biomedical research abstracts from PubMed. It is optimized for biomedical reasoning, text generation, and knowledge extraction.

How it’s used:

• Assists R&D teams in generating hypotheses about drug-target interactions based on literature.

• Used in drug repurposing pipelines to identify unexpected therapeutic links between molecules and diseases.

• Powers semantic search systems within pharma companies to extract relevant publications or trial data.

• Can auto-summarize clinical findings and convert research outputs into structured formats.

Industry adoption:
Pharmaceutical research labs and academic institutions are exploring BioGPT for automating parts of the drug discovery literature workflow, especially during the preclinical research phase.

2. BioBERT / SciBERT

Use in pharma: 

BioBERT is a Transformer-based model designed for scientific and biomedical NLP tasks. BioBERT is trained on biomedical datasets like PubMed, while SciBERT is trained on a broader range of scientific publications.

How they’re used:

• Perform Named Entity Recognition (NER) to extract drugs, diseases, genes, and clinical terms from unstructured text.

• Power clinical trial recruitment systems by matching patient records to trial eligibility criteria.

• Support adverse drug event detection from EHRs, pharmacovigilance reports, and social media monitoring.

• Automate medical coding and document classification tasks for regulatory filings.

Industry adoption:
BioBERT and SciBERT are used in production pipelines by pharma companies like Roche, Merck, and clinical data vendors to process clinical documents, safety reports, and research articles.

3. GPT-4 / GPT-4o (OpenAI via Azure)

Use in pharma:
GPT-4 and GPT-4o are general-purpose LLMs with strong reasoning, summarization, and natural language generation capabilities. Through Azure OpenAI Service, they are available in compliance-ready environments suitable for regulated industries.

How they’re used:

• Generate clinical study reports, regulatory documents, and protocol templates.

• Summarize lengthy trial data, case narratives, and adverse event logs.

• Build internal AI assistants for medical affairs, regulatory, and R&D teams.

• Process structured and unstructured data from PDFs, spreadsheets, and reports for review automation.

Industry adoption:
Companies like Pfizer, AstraZeneca, and Novartis are using GPT-4 via Microsoft Azure for secure enterprise deployments. GPT-4 is also integrated into AI tools for pharmacovigilance and real-time medical writing.

4. Claude 3 (Anthropic)

Use in pharma:
Claude 3  is an LLM known for high interpretability, reduced hallucination, and strong document-level reasoning. It is suitable for pharma use cases requiring traceability and low-risk responses.

How it’s used:

• Summarizes regulatory submissions, SOPs, and quality management documents.

• Answers complex questions about clinical trial protocols or regulatory frameworks.

• Extracts structured data from legacy PDF documents and converts them into standardized formats.

• Powers internal bots that assist QA, compliance, and regulatory affairs teams in navigating company-specific processes.

Industry adoption:
Claude 3 is being tested by clinical data services companies and AI solution providers for document QA, internal knowledge retrieval, and compliance support. Its alignment with safe responses makes it suitable for GxP workflows.

5. Med-PaLM 2 (Google DeepMind)

Use in pharma:
Med-PaLM 2 is a specialized medical LLM trained on curated clinical, biomedical, and patient-focused datasets. It is the first model to exceed 85% accuracy on the USMLE, showcasing its potential for real-world medical reasoning.

How it’s used:

• Evaluated as a clinical decision support tool in hospital and research settings.

• Used in chatbots and digital assistants for physicians to answer diagnostic and treatment queries.

• Summarizes lab test results, medication guides, and diagnostic criteria with human-level accuracy.

• Generates patient-friendly explanations of complex clinical documents.

Industry adoption:
Although not fully commercialized, Med-PaLM 2 has been tested in pilot programs with Mayo Clinic and is being explored by health systems and pharma innovation teams for potential applications in clinical support and medical education.

6. Galactica (Meta)

Use in pharma:
Galactica was trained on a multimodal corpus of scientific data including academic papers, chemical structures, protein sequences, and LaTeX-based scientific formats.

How it’s used:

• Helps with molecule reasoning, enabling the prediction of biological activity or drug interactions.

• Generates scientific summaries with embedded citations for new molecule candidates.

• Enables rapid scanning of large datasets like ChEMBL, PDB, or clinical trial registries.

Industry adoption:
Galactica was briefly released and then pulled due to hallucination concerns. However, research teams in biotech and cheminformatics explored it for target discovery and molecular modeling tasks. The underlying approach continues to influence newer models in the scientific AI domain.

7. OpenBioLLM

Use in pharma:
OpenBioLLM is a newer, open-source biomedical LLM project aimed at providing accessible and customizable models for researchers and startups.

How it’s used:

• Enables custom entity recognition pipelines for biomedical terms.

• Used to build low-cost question answering systems across clinical trial databases.

• Suitable for internal pharma teams looking to fine-tune models on proprietary datasets without relying on external APIs.

Industry adoption:
Still in early stages, but actively used by academic medical research teams, biotech startups, and AI-for-health platforms looking for open-source alternatives to GPT-based models.

Key Applications of LLMs in the Pharmaceutical Industry

Large Language Models (LLMs) are being rapidly adopted across the pharmaceutical value chain to solve high-impact problems at scale. Below is a breakdown of core application areas where LLMs deliver measurable improvements in accuracy, efficiency, and compliance.

1. Patient-Facing Drug Support and Education

Use Cases:

• Personalized Drug Guidance: LLMs deliver context-aware responses to patient queries regarding medication names, usage instructions, contraindications, and missed doses.

• Simplified Medical Language: Convert complex prescription language into patient-friendly summaries, increasing comprehension and adherence.

• Multilingual and Style-Adaptive Communication: Customize tone, format, and detail level of drug information to match individual patient literacy and preferences.

• Medication FAQs and Chatbots: Power self-service portals that answer common questions around dosing, drug interactions, and availability—based on retrieval-augmented generation (RAG) architectures.

• Adverse Effect Handling: Provide step-by-step guidance on what to do in case of side effects or missed doses.

2. Clinical Trials and Real World Evidence (RWE)

Use Cases:

• EHR-Based Patient Matching: LLMs process unstructured clinical notes to extract relevant features (diseases, comorbidities, treatments) and match them to clinical trial eligibility criteria.

• Eligibility Reasoning via Prompt Chains: Use Chain-of-Thought prompting to evaluate inclusion/exclusion criteria and identify suitable patient cohorts.

• Protocol Design and Risk Prediction: Analyze historical RWE datasets to refine trial endpoints, estimate dropout risks, and identify underrepresented subpopulations.

• Literature Synthesis for Trial Planning: Conduct automated systematic reviews to inform trial design and reduce planning cycles.

3. Drug Discovery and Molecular Research

Use Cases:

• Literature Mining for Hypothesis Generation: Process millions of PubMed and bioRxiv abstracts to uncover new drug-target interactions or repurposing opportunities.

• Chemical Structure Generation: Use natural language prompts to generate SMILES codes, predict IUPAC names, and suggest optimized molecular structures.

• Protein and Pathway Analysis: Retrieve biological information on protein targets, gene pathways, and binding affinities.

• Affinity and Interaction Prediction: Guide researchers by predicting molecular interactions and possible off-target effects based on available datasets.

4. Regulatory Compliance and Quality Assurance

Use Cases:

• Regulatory Document Summarization: Convert lengthy SOPs, FDA/EMA guidance documents, or ICH guidelines into concise, actionable summaries.

• Submission Drafting Support: Auto-generate first drafts of Clinical Study Reports (CSRs), Investigational New Drug (IND) applications, and adverse event reports.

• Live Regulatory Monitoring: Use connected LLM agents to stay updated on evolving global compliance requirements in real time.

• QA and GxP Alignment: Parse QA documents, conduct deviation root cause analysis, and maintain audit-readiness using traceable, aligned responses.

5. Knowledge Management and Semantic Search

Use Cases:

• Internal Document Retrieval: Implement semantic search engines across SOPs, regulatory submissions, research papers, and trial logs using LLM embeddings.

• Q&A Assistants for Internal Teams: Deploy LLM-powered agents to support R&D, compliance, and regulatory teams in locating relevant information from unstructured knowledge bases.

• Cross-Document Data Extraction: Automate the extraction of key parameters, clinical codes, or study endpoints from thousands of legacy PDFs or scanned documents.

6. Medical Writing and Publication Automation

Use Cases:

• Clinical Narrative Generation: Draft adverse event narratives and patient summaries from structured trial data.

• Publication Drafting: Generate abstracts, case study synopses, and peer-reviewed articles with references.

• Multilingual Report Translation: Translate regulatory or patient materials while preserving clinical accuracy and tone.

7. Decision Support for Physicians and Field Teams

Use Cases:

• Diagnostic Assistance: Power medical assistants that interpret lab reports, offer treatment pathways, or explain diagnostic results.

• Field Rep Enablement: Equip MSLs (Medical Science Liaisons) with real-time insights from clinical trial databases, safety updates, and published literature.

• Patient History Summarization: Condense years of medical records into short, medically accurate digests for faster physician reviews.

Summary Table of LLM used 

Conclusion: The Future of LLMs in Pharma

Large Language Models (LLMs) are no longer experimental tools—they are rapidly becoming essential infrastructure across the pharmaceutical value chain. From hypothesis generation and molecule modeling to regulatory compliance and medical writing, LLMs are accelerating workflows, reducing human error, and unlocking new possibilities for innovation.

Specialized models like BioGPT, BioBERT, and Med-PaLM 2 demonstrate the power of domain-specific training, while general-purpose models like GPT-4 and Claude 3 bring scale and flexibility to internal operations. Open-source initiatives such as OpenBioLLM are also democratizing access, enabling startups and research teams to experiment with custom biomedical solutions without relying on proprietary APIs.

As data privacy regulations tighten and the need for interpretability grows in regulated environments, models that offer traceability, alignment, and low hallucination risk will become even more valuable. Looking ahead, we can expect tighter integration between LLMs and structured databases (e.g., EHRs, trial registries), more rigorous compliance frameworks, and growing adoption of multi-agent systems that automate end-to-end pharmaceutical tasks.

In short, LLMs are not just enhancing pharma workflows—they are transforming how research, development, and patient care are delivered in the digital age.