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Why AI can transform pharma

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Why AI can transform pharma

TL;DR

  • AI is being rapidly adopted across industries, including pharmaceuticals, to improve efficiency and productivity.
  • AI has been used in pharma research for decades, particularly for identifying treatment targets and molecule design.
  • Google DeepMind's AlphaFold2, which predicts protein structures, was a watershed moment for AI in life sciences.
  • AI is expected to increase the proportion of new molecular entities developed by AI-driven platforms from 12% to 60% within a decade.
  • AI is projected to cut the time to pre-clinical testing from 4-5 years to 12-18 months, and also lead to cost savings.
  • AI can generate synthetic data, potentially allowing human trials to begin sooner, subject to regulatory approval.
  • AI helps improve drug quality by designing molecules with better safety profiles and reducing the failure rate in drug development.
  • AI's ability to process data at scale is crucial for understanding complex human biology and automating solutions.
  • AI can untangle response differences among patients to personalize medical interventions.
  • Cell and gene therapies are identified as a niche where AI could be particularly useful for drug development.
  • Despite AI's advancements, time is still needed to observe the impact and efficacy of molecules in patients.
  • Current clinical trials reflect AI capabilities from 5-10 years ago, indicating future advancements will be seen in the clinic over the next decade.