Blogs

The Ingentium methodology integrates indication-specific knowledge bases and knowledge graphs with Retrieval Augmented Generation (RAG). By employing these resources, it optimizes both graph RAG and prompt generation for refining Large Language Models (LLMs). This comprehensive approach presents numerous advantages for biotech and pharmaceutical firms actively involved in pioneering drug discovery and the repurposing of pharmaceuticals. Customization for Disease Expertise: Ingentium tailors existing LLMs into disease expert models, enhancing their capability for specific diseases[1]. Highly Customized Knowledge Management: Ingentium’s Knowledge Platform is designed to deliver personalized knowledge management solutions, catering to…

In the evolving landscape of drug discovery, medical research and precision medicine, the integration of disease-focused knowledge graphs with Graph Retrieval-Augmented Generation (Graph RAG) presents a transformative approach to Generative AI. This approach enables the creation of specific, disease-expert Large Language Models (LLMs) that possess an in-depth understanding of a specific disease, gleaned from comprehensive, disease-focused knowledge graphs.  This approach leverages the years of experience that Ingentium has developed in the creation of disease-focused knowledge graphs, with the recent advances in the development of Large Language Models.   The Melanoma Knowledge…

At Ingentium, we are at the forefront of employing advanced AI techniques to spearhead innovations in medical research, drug discovery, and personalized medicine. One of our groundbreaking initiatives involves the use of knowledge graph-based fine tuning of open-source Large Language Models (LLMs), such as Mistral7b, specifically tailored for complex diseases like melanoma. This blog post delves into how we harness knowledge graphs to create highly specialized LLMs, focusing on the melanoma use case, and explores the profound implications of these models in the realm of healthcare. Fine-Tuning LLMs with a…

In the complex world of pharmaceutical research, the need for precision and efficiency has led Ingentium to develop a sophisticated approach to generative artificial intelligence (AI). Our Large Language Models (LLMs) are specifically customized to meet the specific demands of biotech and pharmaceutical research, offering a specifically tailored tool for data analysis and insight generation. The Ingentium Edge: Precision-Driven Customization Recognizing the unique challenges of scientific research, Ingentium’s Knowledge Platform is engineered to deliver highly customized knowledge management.  Our Knowledge Platform is a state-of-the-art system that aggregates, organizes, and semantically…

Machine learning models have the potential to revolutionize the way we identify, curate, and analyze disease-specific content. These models can help us extract relevant information from large amounts of data, providing us with a more accurate and efficient way of creating disease-specific knowledgebases. One of the key benefits of using machine learning models to identify disease-specific content is the ability to automate the process of data curation. Traditional methods of curation are time-consuming, and often require a team of experts to manually review and extract relevant information from large amounts…

In the ever-evolving world of biology and healthcare, researchers face complex challenges that demand innovative solutions. Enter custom knowledge graphs – an emerging tool that promises to revolutionize how we navigate biological data and extract valuable insights. In this blog post, we delve into the transformative potential of Ingentium’s knowledge graph platform, which enables researchers to build custom knowledge graphs, tailored to their specific biological questions.   Unveiling the Ingentium Knowledge Graph Platform The Ingentium knowledge graph platform represents a cutting-edge approach to consolidating and analyzing biological information. This AI-powered…

Drug repurposing, also known as drug repositioning, is the process of identifying new therapeutic uses for existing drugs. This approach has the potential to speed up drug development and reduce costs, as drugs that have already been approved for one indication can be repurposed for others without the need for extensive preclinical and clinical trials. In recent years, knowledge graphs and graph analytics have emerged as powerful tools for drug repurposing. A knowledge graph is a structured representation of knowledge, where entities (such as drugs and diseases) are represented as…