Improving route development using convergent retrosynthesis planning | JoVE Visualize

Area of Science:

Computational Chemistry
Medicinal Chemistry
Organic Synthesis

Background:

Computer-aided synthesis planning typically generates individual routes from target molecules to building blocks.
Existing methods do not efficiently leverage shared synthetic pathways for multiple targets, limiting library synthesis in medicinal chemistry.
Convergent synthesis, which maximizes shared intermediates, is crucial for efficient library production.

Purpose of the Study:

To develop a novel computational approach for planning convergent retrosynthetic routes for multiple target molecules simultaneously.
To enhance the efficiency and practical applicability of retrosynthetic planning in medicinal chemistry library synthesis.
To identify and analyze complex synthesis routes with shared intermediates from real-world datasets.

Main Methods:

Utilized a graph-based processing pipeline to analyze Johnson & Johnson Electronic Laboratory Notebooks (J&J ELN) and public datasets.
Developed a multi-target, multi-intermediate planning approach guided by machine learning single-step retrosynthesis models.
Evaluated the approach by identifying convergent routes and assessing their solvability.

Main Results:

Over 70% of reactions analyzed were part of convergent synthesis, covering over 80% of J&J ELN projects.
The novel planning approach identified convergent routes for over 80% of test cases with individual compound solvability exceeding 90%.
Convergent search enabled synthesizing nearly 30% more compounds simultaneously compared to individual search methods, increasing common intermediate utilization.

Conclusions:

The developed convergent synthesis planning approach significantly enhances efficiency and practical applicability in medicinal chemistry.
Convergent retrosynthetic strategies are prevalent and highly effective, with the new method demonstrating high solvability and increased compound yield.
This approach offers a powerful tool for optimizing library synthesis by maximizing convergence and shared intermediates.