LLMs in the Lab: GenAI Transforming Preclinical Research and Scientific Literature Review

In preclinical research, where speed and accuracy are paramount, scientists often find themselves buried in vast volumes of scientific literature, internal reports, and experimental data. The effort to process all this information—while staying compliant and current—can slow down decision-making at a time when speed matters most. That’s where Generative AI (GenAI), and Large Language Models (LLMs) in particular, are beginning to make a difference.

From literature review to assessing safety, efficacy and pharmacokinetic parameters, the preclinical phase presents a clear opportunity to apply domain-specific generative AI in ways that are both practical and impactful. With the right guardrails, GenAI can help bring structure to unstructured data, enabling faster and more reliable insights at the preclinical stage.

The Shift: From Search to Structured Understanding 

Traditional literature discovery tools rely heavily on keyword-based retrieval. While useful, these approaches fall short in parsing nuance, context, and relationships across publications. In contrast, domain-specific LLMs can interpret complex scientific language, link related findings, and generate contextual summaries that go beyond surface-level search.

To be truly effective in regulated research settings, however, generative models must offer more than just speed. They need to be explainable, traceable, and accurate—especially when informing decisions around targets, biomarkers, and study designs. That’s where Retrieval-Augmented Generation (RAG) frameworks come in.

A RAG architecture combines the generation capabilities of LLMs with search tools that pull verified information from biomedical sources. This ensures outputs are grounded in evidence and that users can trace conclusions back to their origins—an essential capability in regulated domains.

Bridging the Gap 

Building GenAI solutions that support scientific discovery requires more than plugging in a general-purpose model. It involves deep domain alignment, secure infrastructure, and integration with real-world workflows used by research teams.

At Innominds, we design and deploy GenAI solutions tailored for regulated industries like Life Sciences. Our platforms are built to deliver scientifically accurate, traceable, and referenceable outputs, especially in data-heavy environments like preclinical research.

Our team has expertise in integrating custom domain-tuned LLMs with curated libraries of scientific, clinical, and regulatory documents, leveraging the RAG framework for referenceable and auditable outputs. Designed to meet compliance and security needs, these systems can help users understand and verify the outputs.

We enable pharma and life sciences organizations to accelerate insight generation while ensuring adherence to R&D regulations, Good Learning Practices (GLP) and compliance standards.

Industry Momentum

The pharmaceutical industry is moving quickly from pilot to production with the use of GenAI in research. A 2024 McKinsey report indicates that GenAI has helped leading pharma companies bring down literature review time by 50%, enabling faster R&D cycles and more confident early-phase decision-making.
Today, LLMs are being adapted to identify potential biomarkers, assist in preclinical study design, and support early-stage hypothesis generation. To meet compliance demands, several global players are investing in private knowledge bases and domain-specific model tuning to ensure adherence to regulatory, privacy, and scientific standards.

We enable pharma and life sciences organizations to accelerate insight generation while ensuring adherence to R&D regulations, Good Learning Practices (GLP) and compliance standards.

Looking Ahead

Gen AI and LLMs are not here to replace researchers, but they will increasingly support them. As preclinical R&D becomes more data-driven, GenAI tools will be essential for navigating complexity, reducing cycle times, and enabling better-informed decisions. The opportunity lies in developing purpose-built, explainable AI systems that operate within the scientific rigor and regulatory governance boundaries.

At Innominds, we bring hands-on experience in building GenAI applications that balance speed with scientific rigor, helping pharma companies transform knowledge bottlenecks into insight-led workflows.

To explore how GenAI can support your preclinical research or knowledge management efforts, write to us: marketing@innominds.com or visit www.innominds.com