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Molecular machine learning (ML) has proven important for tackling variousmolecular problems, such as predicting molecular properties based on moleculardescriptors or fingerprints. Since relatively recently, graph neural network(GNN) algorithms have been implemented for molecular ML, showing comparable orsuperior performance to descriptor or fingerprint-based approaches. Althoughvarious tools and packages exist to apply GNNs in molecular ML, a new GNNpackage, named MolGraph, was developed in this work with the motivation tocreate GNN model pipelines highly compatible with the TensorFlow and Kerasapplication programming interface (API). MolGraph also implements a chemistrymodule to accommodate the generation of small molecular graphs, which can bepassed to a GNN algorithm to solve a molecular ML problem. To validate theGNNs, they were benchmarked against the datasets of MoleculeNet, as well asthree chromatographic retention time datasets. The results on these benchmarksillustrate that the GNNs performed as expected. Additionally, the GNNs proveduseful for molecular identification and improved interpretability ofchromatographic retention time data. MolGraph is available athttps://github.com/akensert/molgraph. Installation, tutorials andimplementation details can be found athttps://molgraph.readthedocs.io/en/latest/.

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