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Data Checking of Asymmetric Catalysis Literature Using a Graph Neural Network Approach.

Eduardo Aguilar-Bejarano¹, Viraj Deorukhkar², Simon Woodward¹

¹GSK Carbon Neutral Laboratories for Sustainable Chemistry, Jubilee Campus, University of Nottingham, Triumph Road, Nottingham NG7 2TU, UK.

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Summary

This summary is machine-generated.

Graph Neural Networks (GNNs) automate the detection of stereochemical errors in chemical databases, significantly reducing manual curation time. This method enhances data reliability by flagging potential misassignments for expert review.

Keywords:

database curation graph neural networks stereochemical misassignment

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Area of Science:

Organic Chemistry
Computational Chemistry
Data Science

Background:

Chemical databases have grown substantially, but data quality is often compromised by human errors.
Manual curation of large chemical datasets is time-consuming and inefficient.
Automated tools are needed to improve the accuracy and reliability of chemical data.

Purpose of the Study:

To develop and apply Graph Neural Networks (GNNs) for identifying stereochemical misassignments in asymmetric catalysis literature.
To create an automated method for enhancing the quality control of chemical databases.

Main Methods:

An ensemble of GNN models was employed to predict the stereoselectivity of reactions.
Discrepancies between predicted and reported outcomes were flagged as potential stereochemical misassignments.
The methodology was tested on databases of asymmetric 1,4-addition reactions using bisphosphine and diene ligands.

Main Results:

The GNN approach successfully identified potential stereochemical misassignments in the studied databases.
This automated method significantly reduced the need for extensive manual data checking.
Expert review was reduced to 2.2% and 3.5% of the total data points in the investigated datasets.

Conclusions:

Graph Neural Networks offer an effective and efficient solution for identifying stereochemical errors in chemical literature.
This methodology accelerates database curation and improves overall data quality.
The approach is valuable for maintaining the integrity of chemical information in scientific literature.