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Unbiasing Retrosynthesis Language Models with Disconnection Prompts.

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Chemists can now guide retrosynthesis with disconnection prompts, leading to more diverse and creative precursor suggestions. This AI approach overcomes data biases and improves predictions by 39%.

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Area of Science:

  • Computational Chemistry
  • Artificial Intelligence in Chemistry

Background:

  • Current data-driven retrosynthesis methods lack user control and diversity.
  • Existing models often recommend unintuitive disconnection strategies, limited by training data biases.

Purpose of the Study:

  • To enhance user interaction and prediction diversity in data-driven retrosynthesis.
  • To introduce a prompt-based chemical language modeling approach for improved retrosynthetic recommendations.

Main Methods:

  • Extended natural language processing prompt-based inference to chemical language modeling.
  • Developed a two-stage schema: automatic disconnection site identification followed by reactant set prediction.
  • Utilized disconnection prompts to steer model predictions.

Main Results:

  • Achieved a 39% performance improvement over baseline retrosynthetic recommendations.
  • Demonstrated a significant increase in class diversity compared to baseline methods.
  • Successfully mitigated prediction biases stemming from training data.

Conclusions:

  • Prompt-based chemical language modeling empowers chemists with greater control over retrosynthesis.
  • The approach yields more diverse, creative, and usable building blocks, enhancing the digital user experience.
  • Applicable across various chemistry domains, including traditional and enzymatic reactions.