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On failure modes in molecule generation and optimization.

Philipp Renz1, Dries Van Rompaey2, Jörg Kurt Wegner2

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Generative models for drug design show promise but have hidden flaws. Current evaluation metrics fail to detect these unintended failure modes in molecular generation and optimization.

Keywords:
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Area of Science:

  • Artificial intelligence
  • Drug discovery
  • Computational chemistry

Background:

  • Deep learning advances have spurred generative models for molecules.
  • These models optimize chemical compounds for specific properties or biological activity.
  • Evaluating generative models in drug design is complex.

Purpose of the Study:

  • To identify and highlight unintended failure modes in molecular generation and optimization.
  • To assess the limitations of current performance metrics and scoring functions.
  • To improve the reliability of generative models in drug design.

Main Methods:

  • Analysis of common generative model architectures.
  • Review of existing performance metrics and scoring functions.
  • Case studies of molecular generation and optimization failures.

Main Results:

  • Identified several failure modes that evade standard evaluation.
  • Demonstrated how current metrics can provide a false sense of security.
  • Highlighted the gap between model performance and practical drug design needs.

Conclusions:

  • Current evaluation metrics for generative models are insufficient for drug design.
  • Need for more comprehensive assessment strategies to ensure model reliability.
  • Further research is required to develop robust evaluation methods for molecular generation.