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Tango*: constrained synthesis planning using chemically informed value functions.

Daniel Armstrong1, Zlatko Jončev1, Jeff Guo1,2

  • 1Ecole Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland daniel.armstrong@epfl.ch philippe.schwaller@epfl.ch.

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Summary
This summary is machine-generated.

A new guided search method, Tango*, efficiently solves the starting material-constrained synthesis planning problem. By optimizing a single hyperparameter, Tango* improves upon existing methods for computer-aided synthesis planning (CASP).

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Area of Science:

  • Computational Chemistry
  • Organic Synthesis
  • Artificial Intelligence in Chemistry

Background:

  • Computer-aided synthesis planning (CASP) has advanced retrosynthetic pathway generation for uncomplicated molecules.
  • Existing methods for starting material-constrained synthesis planning often employ specialized bidirectional search algorithms.
  • A gap exists in efficiently solving synthesis planning problems with specified starting materials using unidirectional search.

Purpose of the Study:

  • To introduce a novel guided search algorithm, Tango*, for the starting material-constrained synthesis planning problem.
  • To demonstrate that Tango* can effectively utilize existing unidirectional search algorithms like Retro*.
  • To improve the efficiency and success rate of constrained retrosynthetic analysis.

Main Methods:

  • Development of a simple guided search algorithm named Tango*.
  • Integration of Tango* with the unidirectional search algorithm Retro*.
  • Optimization of a single hyperparameter within the Tango* algorithm.
  • Utilization of a computed node cost function to guide synthesis pathways.

Main Results:

  • Tango* successfully solves the starting material-constrained synthesis planning problem.
  • The optimized Tango* algorithm demonstrates superior efficiency compared to existing methods.
  • Tango* achieves a higher solve rate in constrained synthesis planning tasks.
  • The node cost function effectively steers the synthesis pathways generated.

Conclusions:

  • Tango* offers an efficient and effective solution for starting material-constrained synthesis planning.
  • The guided search approach provides a valuable alternative to specialized bidirectional algorithms.
  • Further optimization of hyperparameters and cost functions can enhance CASP capabilities.