Applying AI to Drug Discovery and Development: Issues and Challenges
Pharma has invested heavily in Artificial Intelligence (AI) enabled technologies in the hope they would drive R&D efficiency gains. From identifying new targets and making screening more efficient to de-risking and accelerating clinical trials, AI tools are commonly applied across drug discovery and development. But is AI delivering value? Which AI tools are being applied successfully? What cultural challenges remain? In this report experts assess the value of AI to pharma’s R&D programmes and the challenges that must be overcome.
Companies
AbbVie, GlaxoSmithKline, Pfizer, Leo Pharma, Bristol Myers Squibb, Google, Amazon, Bayer, Insilico Medicine, Alphabet, Microsoft, McKinsey & Co, Netflix, Flatiron Health, Owkin, Recursion, Meta, DeepMind, Exscientia, Hugging Face, Morgan Stanley, PathAI, Proscia, Tempus, University of Washington
- Subject synopsis
- Research methodology and objectives
- Key insights summary
- Issues and insights
- What is driving pharma’s investment in AI tools?
- Issue summary
- Questions
- Key insights
- Supporting quotes
- What are the benefits of applying AI tools to drug discovery and development?
- Issue summary
- Questions
- Key insights
- Supporting quotes
- Intelligence exhibits
- AI’s impact on the organisation
- Issue summary
- Questions
- Key insights
- Supporting quotes
- Intelligence exhibits
- The challenges of applying AI to drug discovery and development
- Issue summary
- Questions
- Key insights
- Supporting quotes
- Where we are and where we are going
- Issue summary
- Questions
- Key insights
- Supporting quotes
- Intelligence exhibits
- Further reading
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