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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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Harnessing Medicinal Chemical Intuition from Collective Intelligence.

Pierre Llompart1,2, Kwame Amaning1, Marc Bianciotto1

  • 1Molecular Design Sciences, Integrated Drug Discovery, Sanofi, 1 Imp. Des Ateliers, Vitry-sur-Seine 94400, France.

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Summary
This summary is machine-generated.

Collective intelligence enhances drug discovery decision-making for ADMET endpoints, outperforming artificial intelligence in most areas. Combining human and artificial intelligence offers a synergistic approach for improved project outcomes.

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Area of Science:

  • Drug discovery and development
  • Computational chemistry
  • Medicinal chemistry

Background:

  • Decision-making in drug discovery often relies on individual medicinal chemistry intuition.
  • Collective intelligence, emerging from group efforts, has shown promise in complex problem-solving.
  • The application of collective intelligence to drug discovery, specifically lead optimization, remains underexplored.

Purpose of the Study:

  • To explore the application of collective intelligence in drug discovery lead optimization.
  • To compare the performance of a collective intelligence agent against an artificial intelligence model.
  • To assess the synergy between human and artificial intelligence in drug discovery decision-making.

Main Methods:

  • Anonymously surveyed 92 researchers from Sanofi with diverse expertise on ADMET-related questions.
  • Developed a collective intelligence agent based on researcher feedback.
  • Compared the collective intelligence agent's performance against an artificial intelligence model for various endpoints.

Main Results:

  • Collective intelligence significantly improved decision-making for optimizing ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) endpoints compared to individual judgments.
  • The collective intelligence agent outperformed the artificial intelligence model across most endpoints, with the exception of hERG inhibition.
  • A complementary relationship was observed between collective human intelligence and artificial intelligence.

Conclusions:

  • Collective intelligence offers a valuable tool for enhancing decision-making in drug discovery lead optimization.
  • A synergistic approach combining human and artificial intelligence holds significant potential for improving project decision-making in drug discovery.
  • Further research into integrating collective human intelligence with AI can accelerate the drug discovery process.