Driving success in personalized medicine through AI-enabled computational modeling

Area of Science:

• Pharmacology
• Computational Biology
• Drug Development

Background:

• Drug development is costly and lengthy due to high clinical attrition rates.
• The bench-to-clinic translation gap, especially in personalized medicine, contributes to development failures.

Purpose of the Study:

• To highlight the role of artificial intelligence (AI)-driven platforms integrated with mechanistic modeling in drug development.
• To demonstrate how AI can mitigate deficiencies in traditional drug development processes.

Main Methods:

• Leveraging ubiquitous data across various drug development phases.
• Integrating artificial intelligence (AI) with mechanistic modeling.
• Reducing human intervention in the drug discovery pipeline.

Main Results:

• AI-driven platforms accelerate the drug development process.
• AI integration bridges the translational gap between drug discovery and clinical application.
• AI helps uncover connections between drugs and diseases more effectively.

Conclusions:

• AI-powered platforms are instrumental in overcoming challenges in drug development.
• Integrating AI with mechanistic modeling enhances the efficiency and translatability of drug candidates.
• AI offers a promising approach to reduce costs and timelines in pharmaceutical research.