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MolScribe: Robust Molecular Structure Recognition with Image-to-Graph Generation.

Yujie Qian¹, Jiang Guo¹, Zhengkai Tu²

¹Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.

Journal of Chemical Information and Modeling

|March 27, 2023

Summary

This summary is machine-generated.

MolScribe accurately converts molecular images into graph structures by predicting atoms, bonds, and their layouts. This novel model enhances chemical structure recognition and is publicly available.

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Area of Science:

Computational chemistry
Cheminformatics
Machine learning

Background:

Automating molecular structure recognition from chemical literature is challenging due to diverse drawing styles.
Existing methods struggle with variations in chemical diagrams.

Purpose of the Study:

To introduce MolScribe, a novel image-to-graph generation model for molecular structure recognition.
To improve the accuracy and robustness of converting molecular images to graph structures.

Main Methods:

MolScribe explicitly predicts atoms, bonds, and their geometric layouts.
Incorporates symbolic chemistry constraints for chirality recognition and structure expansion.
Utilizes data augmentation strategies to enhance robustness against domain shifts.

Main Results:

MolScribe significantly outperforms previous models on synthetic and realistic molecular images.
Achieved 76-93% accuracy on public benchmarks for molecular structure recognition.
Provides confidence estimation and atom-level alignment for easy verification by chemists.

Conclusions:

MolScribe offers a robust and accurate solution for molecular image-to-graph conversion.
The model's interpretability aids chemists in verifying predictions.
Public availability via Python and web interfaces facilitates broader adoption.