Target Identification and Selection in Drug Discovery: Plugging into the Power of AI and Knowledge Graphs

Summary

This whitepaper presents a smart solution that leverages artificial intelligence (AI) and knowledge graph technology to refine the process of target identification and selection in drug discovery. We explore the benefits of using AI algorithms, the value of knowledge graphs, and the integration of Large Language Models (LLMs) and generative AI (genAI) in accelerating the drug discovery journey.

What does the white paper include?

  • What challenges facing target identification and selection
  • How to transform the drug discovery process
  • How to speed up target discovery with AI
  • How to jump on the bandwagon of LLMs and Generative AI
  • How to step up the game with knowledge graphs
  • What are the benefits of AI-powered target identification
  • What are Ontotext’s seven steps roadmaps to success

About the author:
Martina Markova is Ontotext’s Product Manager of Target Discovery and Business Analyst in Ontotext’s Life Sciences and Healthcare department. She brings experience from both the clinical and preclinical drug development phases. She has helped top pharma companies, CROs, academia, and biotech startups in their journeys to leverage cutting-edge digital technologies successfully.

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