Implementation of a data science and artificial intelligence strategy across pharmaceutical R&D: challenges and...

Area of Science:

• Pharmaceutical research and development
• Data science
• Artificial intelligence

Background:

• The pharmaceutical industry faces increasing pressure to innovate and accelerate drug discovery.
• The integration of data science and artificial intelligence (DS&AI) is crucial for maintaining competitiveness.

Purpose of the Study:

• To present an operational framework for implementing DS&AI in pharmaceutical R&D.
• To address the challenges and propose solutions for realizing the potential of DS&AI.

Main Methods:

• Review of current DS&AI applications in pharmaceutical R&D.
• Development of a conceptual operational framework.
• Identification of key challenges and corresponding solutions.

Main Results:

• The proposed framework provides a structured approach to DS&AI integration.
• Key challenges include data accessibility, talent acquisition, and regulatory hurdles.
• Solutions involve strategic data governance, interdisciplinary team building, and adaptive regulatory strategies.

Conclusions:

• Implementing DS&AI in pharmaceutical R&D requires a robust operational framework.
• Overcoming identified challenges is essential for successful adoption and innovation.
• The framework facilitates the realization of DS&AI's transformative potential in drug development.