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Precision fragment addition: domain-specific DeepFrag2 models for smarter lead optimization.

César R García-Jacas1, Harrison Green1, Shayne D Wierbowski2

  • 1Department of Biological Sciences, University of Pittsburgh Pittsburgh PA USA durrantj@pitt.edu.

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Summary

This study enhances DeepFrag, a tool for small-molecule drug optimization, by developing new machine-learning models. These models improve fragment prediction accuracy for medicinal chemists, aiding drug discovery.

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Machine learning

Background:

  • Small-molecule lead optimization is crucial in drug discovery.
  • Fragment-based drug design (FBDD) is a key strategy.
  • Accurate prediction of optimizing fragments accelerates lead optimization.

Purpose of the Study:

  • To introduce enhanced machine-learning models for fragment-based lead optimization.
  • To improve the accuracy of predicting optimizing molecular fragments.
  • To provide tools for medicinal chemists with specific optimization insights.

Main Methods:

  • Development of new DeepFrag models based on previous work.
  • Training models to predict fragments with specific sizes and chemical properties.
  • Fine-tuning models on specific drug-target receptor classes.

Main Results:

  • Demonstrated enhanced accuracy in predicting optimizing fragments.
  • Showed improved DeepFrag model performance when fine-tuned on receptor classes.
  • Developed targeted models for specific fragment characteristics and drug targets.

Conclusions:

  • The enhanced DeepFrag models offer improved accuracy for fragment-based lead optimization.
  • Targeted models benefit medicinal chemists with prior knowledge of fragment properties or drug targets.
  • DeepFrag2 is released under the open-source MIT license to promote adoption.