Generative AI and LLMs for Biopharma R&D

Translating the power of Generative AI and LLM into practical and usable solutions within and beyond R&D.

The “omics explosion” generates massive datasets across different levels such as genome, transcriptome, epigenome, etc., posing a major challenge for scientists to obtain clinically actionable biological insights. AI/ML-powered foundation models in life sciences can decipher cellular languages by integrating and interpreting these complex datasets. Successfully harnessing this power could unlock clinically relevant biological understanding, accelerating progress in various fields. Our LLM service offering includes the following:

Our LLM service offering includes the following:

Develop chat interface to large GWAS, omics datasets

Summarize learnings from literature reviews and publications


Key components of our LLM service:

Advanced Omics Dataset Interrogation

Providing conversational interfaces to complex genomics, transcriptomic, and multi-omics datasets for scientists to delve deeper into Omics analyses such as GWAS, scRNA-seq, and perturb-seq.

Utilizing AI Foundation Models in Biology

Accelerating complex inquiries using AI foundation models for DNA, RNA, and Proteins such as identifying the impact of non-coding variants on genes and selecting optimal targets for ASO therapies targeting splicing disorders, etc.

Streamlined Target Analysis and Market Intelligence

Leveraging fine-tuned LLM models and prompt libraries to provide specific insights crucial for target analysis, disease studies, and market intelligence, facilitating informed decision-making.

Enhanced Research Efficiency and Insight

Integrating LLMs and foundation models to enhance the efficiency of scientific research. Scientists can quickly access and analyze data, simulate gene knockouts, and gain insights into gene interactions and pathways.

Benefits of using Aganitha’s LLM services

We can help researchers in biopharma using LLM to annotate genes, pathways, and functions, generate hypotheses, interpret spatial data, and summarize relevant information from extensive research articles. Using our services, your researchers can understand network biology and analyze downstream sequencing data with LLMs via conversational interfaces. We can help you to evolve the use of LLMs in the enterprise: pathways, even for new modalities with limited knowledge. We deliver insights into the following aspects of drug design (with our tools):

Intelligent chatbots as research assistants

Overcome the challenge of ever expanding data in the research domain using Retrieval-Augmented Generation (RAG). RAG can create intelligent chatbots to get up-to-date and reliable information to facilitate gene annotation and hypothesis generation.

Develop new targets and therapeutics

Use LLMs to develop new targets and therapeutics by integrating multi-omics data, perform downstream analysis on the sequencing data and generate a targeted information dossier/CV on disease and specific areas.

Automate tasks in diverse spaces

Use LLMs to automate diverse tasks, spanning across scientific, legal, and administrative domains. They can be used to comprehend intricate legal contracts, evaluate them for patentability, and efficiently organize research materials.

Reduce the development cost

Utilize LLMs to automate the integration of various data sources at a semantic level eliminating the need for manual data manipulation. LLMs orchestrate reasoning to leverage existing agents and bots, reducing the necessity for writing code, and can replace complex user interfaces with conversational UIs.


AI-driven single-cell transcriptomics analysis portal

Know your dataset:

Ask diverse questions in simple english to understand your dataset. For example: know the cohorts and samples captured in the dataset, number of cells before and after the treatment, etc.

Visualize your dataset:

Use interactive visualizations to understand correlations between different features. For example: make side-by-side UMAP plots of cells, from samples “before” and “after” treatment, coloring it by cell types and showing a legend.

Multi-omics study:

Integrate multi-omics data to ask predictive, mechanistic, and descriptive questions. For example: identify biomarkers that distinguish different disease states or predict disease progression, and find out all the targets associated with “X” disease?

Perform Meta-analysis:

Use additional statistical power to overcome small effect sizes or sample sizes. For example: compare samples across studies or test the robustness of a single set of conclusions.

Discover our offerings across the biopharma value chain

Learn more about our LLM services