This study analyzes the emergence of AI-based drug repurposing and examines the factors driving and hindering adoption. The limitation of traditional drug discovery has led to the growing interest in AI -based drug repurposing, which offers numerous advantages in terms of time, speed, and cost. AI-based drug repurposing has been explored across different disease indications, such as rare diseases, oncology, metabolic diseases, autoimmune diseases, and neurodegenerative diseases.AI-based Drug Repurposing is emerging as a new and faster approach to bringing drugs to patients.
The study focuses on the different AI-technologies, such as machine learning, deep learning, and generative AI, and how they are enabling AI-based drug repurposing. In addition, the report looks at key participants involved in AI-based drug repurposing, including their AI approaches, disease focus areas, and future outlook. The study examines the key factors driving and restraining the growth of AI-based drug repurposing and identifies the growth opportunities emerging from the changes in this space that key participants and stakeholders can leverage.
Key Questions This Study Answers:
- What are the key drivers and restraints in the development of AI-based drug repurposing?
- What are the applications of AI-based drug repurposing across disease indication?
- What are the key trends in AI-based drug repurposing?
- Who are the key innovators, and what are their approaches to AI-based drug repurposing?
- What does the funding and partnership landscape look like?
Table of Contents
Strategic Imperatives
Growth Opportunity Analysis
Growth Generator
AI Models Enabling Drug Repurposing
Application Across Disease Indications
Key Innovators - AI in Drug Repurposing
Funding and Partnerships
Growth Opportunity Universe
Next Steps
