Insilico Medicine brings AI-powered “ChatPandaGPT” to its target discovery platform
A revolution in drug discovery thanks to a new chat assistant?
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Insilico Medicine, a clinical-stage generative artificial intelligence (AI)-powered drug discovery company, has integrated advanced AI chat functionality based on recent advances in large language models into its PandaOmics platform. The new feature, “ChatPandaGPT,” enables researchers to have natural language conversations with the platform and efficiently navigate and analyze large datasets, facilitating the discovery of potential therapeutic targets and biomarkers in a more efficient manner. Insilico Medicine is the first biotech company to implement chat functionality using large language models into its AI drug discovery platform.
A demonstration of robust answers provided by ChatPandaGPT related to the topic of psoriasis.
Insilico Medicine
PandaOmics is an AI-driven target discovery platform developed by Insilico Medicine that applies deep learning models to identify therapeutic targets associated with a given disease through a combination of omics data analysis put in the context of prior information coming from publications, clinical trials, and grant applications. The algorithm optimizes for the best potential therapeutic targets by scoring results on factors such as novelty, confidence, commercial tractability, druggability, safety, and other key properties that drive target selection decisions.
In November 2022, Insilico Medicine released the addition of a transformer-based knowledge graph to PandaOmics. The neural network of special architecture, the so-called transformer, extracts the relevant information from the list of scientific publications and produces a visually appealing graph that links genes, diseases, chemical compounds, and biological processes. The knowledge graph offers insights into the molecular mechanisms of diseases and the biological activities of genes and compounds in the context of a disease. It also reveals the most promising drug targets and biomarkers.
The new chat functionality, based on large language models, has been integrated with the knowledge graph functionality of PandaOmics and offers a more efficient, accurate, and personalized way to access information on molecular biology, therapeutic target discovery, and pharmaceutical development.
ChatPandaGPT offers additional features over the base on the general-purpose chat models. It draws from a specialized knowledge base, enabling it to provide accurate and detailed information related to molecular biology, therapeutic target discovery, and pharmaceutical development. It uses both natural language processing and machine learning algorithms, allowing it to provide more personalized and relevant responses to users on the platform. ChatPandaGPT is available at all times and can process multiple queries simultaneously without becoming tired or making mistakes.
“Insilico Medicine is excited to integrate this cutting-edge chat functionality into our PandaOmics platform,” said Alex Zhavoronkov, PhD, Founder and CEO of Insilico Medicine. “By enabling researchers to have natural language conversations with the platform, we’re making it easier to navigate complex data and identify potential therapeutic targets and biomarkers.”
PandaOmics is known for its advanced knowledge graph analytics platform, which enables users to discover relationships and insights within complex data structures. By integrating the latest advances in large language models, users can explore data in an even more intuitive and efficient way.
“We’re thrilled to integrate this advanced chat functionality into PandaOmics,” said Frank Pun, PhD, head of the PandaOmics application scientists team. “This integration represents a significant step forward in our efforts to provide researchers with the tools they need to make important discoveries in the field of drug discovery.”
The integration between ChatPandaGPT and PandaOmics is available now, allowing researchers to begin navigating and analyzing large datasets and make important discoveries more efficiently.
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