QMLGDec 19, 2023

New Horizons: Pioneering Pharmaceutical R&D with Generative AI from lab to the clinic -- an industry perspective

arXiv:2312.12482v12 citationsh-index: 2
Originality Synthesis-oriented
AI Analysis

This provides a strategic roadmap for pharmaceutical R&D leaders to implement generative AI, though it's an incremental application of existing AI methods to a specific industry domain.

This perspective paper examines how generative AI can transform pharmaceutical R&D by addressing challenges across the entire value chain, from early discovery to regulatory approval, with potential benefits including increased productivity, accelerated timelines, and improved research quality.

The rapid advance of generative AI is reshaping the strategic vision for R&D across industries. The unique challenges of pharmaceutical R&D will see applications of generative AI deliver value along the entire value chain from early discovery to regulatory approval. This perspective reviews these challenges and takes a three-horizon approach to explore the generative AI applications already delivering impact, the disruptive opportunities which are just around the corner, and the longer-term transformation which will shape the future of the industry. Selected applications are reviewed for their potential to drive increase productivity, accelerate timelines, improve the quality of research, data and decision making, and support a sustainable future for the industry. Recommendations are given for Pharma R&D leaders developing a generative AI strategy today which will lay the groundwork for getting real value from the technology and safeguarding future growth. Generative AI is today providing new, efficient routes to accessing and combining organisational data to drive productivity. Next, this impact will reach clinical development, enhancing the patient experience, driving operational efficiency, and unlocking digital innovation to better tackle the future burden of disease. Looking to the furthest horizon, rapid acquisition of rich multi-omics data, which capture the 'language of life', in combination with next generation AI technologies will allow organisations to close the loop around phases of the pipeline through rapid, automated generation and testing of hypotheses from bench to bedside. This provides a vision for the future of R&D with sustainability at the core, with reduced timescales and reduced dependency on resources, while offering new hope to patients to treat the untreatable and ultimately cure diseases.

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