Generative AI: driving productivity and scientific breakthroughs in pharmaceutical R&D

Area of Science:

• Pharmaceutical Research and Development
• Artificial Intelligence
• Drug Discovery and Development

Background:

• Generative artificial intelligence (GenAI) is rapidly advancing, presenting significant opportunities within the pharmaceutical sector.
• Current applications of AI in R&D include virtual assistants for task automation and advancements in small-molecule drug design.

Purpose of the Study:

• To assess the current and future applications of GenAI in pharmaceutical research and development (R&D).
• To evaluate the mid-term and long-term transformative potential of GenAI in the pharmaceutical industry.

Main Methods:

• Review of current generative AI (GenAI) applications in pharmaceutical R&D.
• Analysis of emerging machine learning (ML) applications, including synthetic data generation.
• Assessment of converging technologies for data capture and hypothesis generation.

Main Results:

• GenAI enhances productivity through virtual assistants and automates routine tasks.
• GenAI advances novel small-molecule drug design and enables new ML applications via synthetic data.
• Future impact includes improved operational efficiencies and digital innovations in drug development.

Conclusions:

• GenAI holds substantial transformative potential for pharmaceutical R&D.
• Next-generation AI is expected to facilitate rapid, automated hypothesis generation and testing.
• The integration of GenAI promises to reshape drug discovery and development processes.