MolGraph: a Python package for the implementation of molecular graphs and graph neural networks with TensorFlow and Keras

• Published in: Journal of computer-aided molecular design Vol. 39; no. 1; p. 3
• Main Authors: Kensert, Alexander, Desmet, Gert, Cabooter, Deirdre
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.12.2025; Springer Nature B.V
• Subjects: Algorithms; Animal Anatomy; Application programming interface; Benchmarking; Benchmarks; Chemistry; Chemistry and Materials Science; Chromatography; Computer Applications in Chemistry; Datasets; Fingerprints; Graph neural networks; Graphs; Histology; Machine Learning; Molecular machines; Molecular properties; Morphology; Neural networks; Neural Networks, Computer; Physical Chemistry; Retention; Retention time; Software; Deep learning; Cheminformatics; Graph neural networks; Bioinformatics; Machine learning
• ISSN: 0920-654X; 1573-4951; 1573-4951
• DOI: 10.1007/s10822-024-00578-w

Molecular machine learning (ML) has proven important for tackling various molecular problems, such as predicting molecular properties based on molecular descriptors or fingerprints. Since relatively recently, graph neural network (GNN) algorithms have been implemented for molecular ML, showing comparable or superior performance to descriptor or fingerprint-based approaches. Although various tools and packages exist to apply GNNs in molecular ML, a new GNN package, named MolGraph, was developed in this work with the motivation to create GNN model pipelines highly compatible with the TensorFlow and Keras application programming interface (API). MolGraph also implements a module to accommodate the generation of small molecular graphs, which can be passed to a GNN algorithm to solve a molecular ML problem. To validate the GNNs, benchmarking was conducted using the datasets from MoleculeNet, as well as three chromatographic retention time datasets. The benchmarking results demonstrate that the GNNs performed in line with expectations. Additionally, the GNNs proved useful for molecular identification and improved interpretability of chromatographic retention time data. MolGraph is available at https://github.com/akensert/molgraph . Installation, tutorials and implementation details can be found at  https://molgraph.readthedocs.io/en/latest/ .