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Computer-Assisted Retrosynthesis Based on Molecular Similarity.

Connor W Coley¹, Luke Rogers¹, William H Green¹

¹Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States.

ACS Central Science

|January 4, 2018

Summary

Molecular similarity effectively proposes retrosynthetic disconnections by learning from known reactions. This computational approach accurately predicts precursors for chemical synthesis without prior chemical knowledge.

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Area of Science:

Computational Chemistry
Synthetic Chemistry
Machine Learning in Chemistry

Background:

Retrosynthesis is crucial for planning chemical synthesis.
Current methods often require expert chemical knowledge or complex rule-based systems.
Developing data-driven approaches for retrosynthesis is an active research area.

Purpose of the Study:

To develop and validate a novel computational method for retrosynthetic analysis.
To demonstrate the efficacy of molecular similarity as a metric for predicting synthetic precursors.
To explore the application of this method for both one-step and multi-step synthesis planning.

Main Methods:

Utilized a large dataset of 40,000 reactions from patent literature.
Employed molecular similarity metrics to identify analogous precedent reactions.
Developed an algorithm to propose and rank one-step retrosynthetic disconnections.
Validated the approach on 5,000 test reactions.

Main Results:

Molecular similarity proved highly effective for proposing retrosynthetic disconnections.
The method successfully identified known reactants within the top 10 proposed precursors in 74.1% of test cases.
The one-step strategy was extended to demonstrate feasibility for multi-step pathway planning.

Conclusions:

Computational retrosynthesis can be effectively achieved using molecular similarity metrics.
This approach reduces the need for explicit encoding of chemical knowledge.
The methodology shows promise for accelerating drug discovery and development through efficient synthesis planning.