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Breadth-first search improves molecular graph generation by enhancing data feature coverage compared to depth-first search in deep generative models. Overtraining negates differences between algorithms.

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Area of Science:

  • Computational chemistry
  • Cheminformatics
  • Machine learning in drug discovery

Background:

  • Deep generative models are increasingly used for molecular graph generation.
  • Graph traversal algorithms influence the structure and properties of generated molecules.

Purpose of the Study:

  • To investigate the impact of breadth-first search (BFS) versus depth-first search (DFS) on molecular graph generation.
  • To compare the performance of BFS and DFS in deep molecular generative models.

Main Methods:

  • Training a graph-based deep molecular generative model.
  • Utilizing BFS and DFS algorithms to determine node order during graph construction.
  • Evaluating generated molecules using metrics such as validity, coverage, and molecular shape on a natural product dataset.

Main Results:

  • BFS traversal resulted in better coverage of training data features than DFS.
  • Quantified differences in molecular validity, coverage, and shape between BFS and DFS.
  • Identified that overtraining leads to identical results for both BFS and DFS.

Conclusions:

  • BFS is a more effective graph traversal strategy for molecular graph generation with deep learning models.
  • The choice of traversal algorithm significantly impacts the quality and diversity of generated molecular structures.
  • Overtraining can mask the benefits of specific graph traversal algorithms in molecular generation.