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Extracting medicinal chemistry intuition via preference machine learning.

Oh-Hyeon Choung1, Riccardo Vianello1, Marwin Segler2

  • 1Novartis Institutes for Biomedical Research, 4002, Basel, Switzerland.

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Artificial intelligence learning-to-rank models were trained on medicinal chemist feedback to accelerate drug discovery lead optimization. These AI tools aid in compound prioritization and de novo drug design, significantly reducing development time.

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Area of Science:

  • Medicinal Chemistry
  • Artificial Intelligence
  • Drug Discovery

Background:

  • Lead optimization in drug discovery is complex, requiring extensive expertise and time from medicinal chemists.
  • Collaborative decision-making for molecular property profiles is a lengthy, expertise-driven process.

Purpose of the Study:

  • To replicate the collaborative lead optimization process using AI.
  • To develop AI models that learn from expert medicinal chemist feedback.
  • To accelerate the drug discovery timeline through intelligent automation.

Main Methods:

  • Applied artificial intelligence learning-to-rank techniques.
  • Utilized feedback from 35 medicinal chemists at Novartis over several months.
  • Trained models on annotated response data.

Main Results:

  • Developed AI proxies that mimic expert decision-making in lead optimization.
  • Demonstrated the utility of learned proxies in compound prioritization.
  • Showcased applications in motif rationalization and biased de novo drug design.

Conclusions:

  • AI-driven approaches can effectively replicate and accelerate expert-driven lead optimization.
  • The developed models and code are available open-source, facilitating wider adoption.
  • This work offers a scalable solution to a time-consuming aspect of drug discovery.