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RGBChem: Image-Like Representation of Chemical Compounds for Property Prediction.

Rafał Stottko¹, Radosław Michalski², Bartłomiej M Szyja¹

¹Institute of Advanced Materials, Faculty of Chemistry, Wrocław University of Science and Technology, Gdańska 7/9, Wrocław 50-344, Poland.

Journal of Chemical Theory and Computation

|May 12, 2025

Summary

RGBChem converts chemical compounds into images to train convolutional neural networks (CNNs) for predicting the highest occupied molecular orbital-lowest unoccupied molecular orbital (HOMO-LUMO) gap. This method significantly improves model accuracy, especially for small datasets.

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Area of Science:

Computational chemistry
Machine learning
cheminformatics

Background:

Predicting molecular properties like the HOMO-LUMO gap is crucial in chemistry.
Machine learning (ML) models require substantial data, which is often limited in chemical research.
Existing methods for converting molecular data into ML-usable formats can be restrictive.

Purpose of the Study:

To introduce RGBChem, a novel method for generating image representations of chemical compounds.
To enhance ML model training by creating diverse data points from single molecules.
To improve the prediction accuracy of the HOMO-LUMO gap using ML.

Main Methods:

Chemical compounds are converted into image representations using the RGBChem approach.
These images are used to train a convolutional neural network (CNN).
The training dataset is expanded by modifying atom order in .xyz files to generate multiple unique images per molecule.

Main Results:

The RGBChem approach enables the generation of multiple unique data points from a single molecule.
Training CNNs with RGBChem-generated data leads to statistically significant improvements in prediction accuracy.
The method effectively addresses challenges posed by small datasets in ML applications for chemistry.

Conclusions:

RGBChem offers a powerful strategy for data augmentation in cheminformatics.
The approach enhances the applicability of ML in chemical research, particularly when data is scarce.
This work demonstrates a significant advancement in leveraging ML for predicting molecular properties.