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

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/.Comment: 14 pages, 4 figures, 4 table

Improving liquid chromatography efficiency: channels structured with micro-pillars

Band dispersion has been measured in micromachined separation channels structured with orderly disposed cylindrical micropillars. It was found that with an optimal channel design the band broadening could be lower by a factor of 3 than in packed columns with a comparable particle size. The positioning of the row of pillars closest to the side wall was a decisive factor in influencing band broadening

Visualization and quantification of the onset and the extent of viscous fingering in micro-pillar array columns

New experimental data of the viscous fingering (VF) process have been generated by studying the VF process in perfectly ordered pillar array columns instead of in the traditionally employed packed bed columns. A detailed quantitative analysis of the contribution of VF to the observed band broadening could be made by following the injected species bands using a fluorescence microscope equipped with a CCD-camera. For a viscosity contrast of 0.16 cP, a plate height increase of about 1 μm can be observed, while for a contrast of respectively 0.5 cP and 1 cP, additional plate height contributions of the order of 5–20 μm were observed. Citing these values is however futile without noting that they also depend extremely strongly on the injection volume of injected sample. It was found that, for a given viscosity contrast of 0.314 cP, the maximal plate height increase varied between 0.5 μm and 18 μm if the injection volume was varied between 3.0 nl and 32.7 nl. These values furthermore also strongly vary with the distance along the column axis