  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 chemistry
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, they were benchmarked against the datasets of MoleculeNet, as well as
three chromatographic retention time datasets. The results on these benchmarks
illustrate that the GNNs performed as expected. 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/.
