use case

Bio Pharma

Learn how scientists can accelerate research work by combining synthesis of private research with latest resources from public or external private forums

Leveraging RAG & Agents for BioPharma/ Drug Discovery Research with Private AI

One of the most significant struggles BioPharma researchers face is the lengthy timeline from initial discovery to market approval. This is due to the complexity of the drug development process, which involves rigorous testing, regulatory hurdles, and often unpredictable outcomes in clinical trials.

Additionally, the need for comprehensive safety and efficacy data prolongs the research timeline.

Researchers often seek ways to streamline processes, utilize innovative technologies, and improve collaboration to accelerate drug discovery and development timelines. However, ensuring thoroughness and maintaining high safety and efficacy standards remain paramount, even in efforts to expedite research.

In this context, we see significant potential for the ethical use of Generative, Private AI models in expediting the research process. Large BioPharma enterprises with several researchers collaborating on drug discovery can use Private AI models in innovative ways to accelerate their processes.

This use case dives into the details, along with a link to documentation that enables you to develop such a model on the DKubeX platform.

Problem Statement

Scientists Need a Way to Combine Private Data with Latest Research from Public Sources:

The amalgamation of proprietary datasets with publicly available research is crucial for gaining comprehensive insights and making informed decisions in drug discovery and BioPharma research. However, ensuring data privacy and security while leveraging this wealth of information poses a significant challenge.

Scientists Need an Easy Way to Leverage RAG and Agents for Their Research Work:

Traditional methods of information retrieval often involve manual searches across disparate sources, leading to inefficiencies and potential oversights. Researchers require a seamless and efficient solution that integrates advanced AI models with RAG and customizable agents to streamline their workflow and enhance productivity.


Scientists and researchers constantly seek innovative solutions to streamline their workflows, merge private datasets with the latest public research, and navigate the vast sea of information available. This is where the combination of the Retrieval-Augmented Generation (RAG) model and intelligent agents comes into play, providing a powerful toolset for researchers to harness the power of AI-driven information retrieval while safeguarding sensitive data through private AI frameworks like DKubeX.

Additionally, researchers need a user-friendly platform to leverage these advancements in their work.

Workflow Demonstration To illustrate the effectiveness of leveraging RAG and agents
  1. Ingesting Private Data: We utilize our in-house proprietary MLOps and LLMOps platform,  DKubeX as our private AI framework to ingest a fixed set of private data. DKubeX ensures data privacy and confidentiality throughout the ingestion process, maintaining the integrity of sensitive information. DKubeX also provides users with custom plugins to scrape data from any online data sources and ingest them into the platform with simple tools.
  2. Querying Multiple Sources on the Fly: With the private data securely ingested, researchers can leverage intelligent agents equipped with RAG capabilities to query multiple sources in real time. These agents, tailored to specific tasks such as retrieving relevant papers from PubMed, conducting internet searches, or accessing proprietary datasets, facilitate dynamic information retrieval based on user queries. The seamless integration of RAG and agents enables researchers to access a diverse range of information efficiently and effectively.

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