A dynamic programming algorithm to predict synthesis processes of tree-structured compounds with graph grammar

Area of Science:

• Computational Chemistry
• Organic Synthesis
• Algorithm Development

Background:

• Predicting organic synthesis paths is crucial but computationally intensive.
• Existing methods often suffer from non-polynomial computational time.
• Need for efficient algorithms for complex molecule synthesis.

Purpose of the Study:

• To develop a novel bottom-up dynamic programming algorithm for predicting synthesis paths.
• To address the computational complexity of existing methods.
• To enable efficient synthesis prediction for tree-structured compounds.

Main Methods:

• Transforming synthesis prediction into an unordered tree generation problem.
• Representing compounds and reactions as unordered trees and rules.
• Utilizing a subclass of Node Label Controlled (NLC) grammars for rule representation.

Main Results:

• A bottom-up dynamic programming algorithm was developed.
• The algorithm transforms synthesis into tree generation.
• Computational results demonstrate the algorithm's effectiveness.

Conclusions:

• The proposed algorithm offers an efficient approach to predicting synthesis paths.
• Dynamic programming and NLC grammars provide a robust framework.
• This method enhances computational feasibility for organic synthesis prediction.