  Quantitative Structure Property Relationship studies aim to define a mapping
between molecular structure and arbitrary quantities of interest. This was
historically accomplished via the development of descriptors which requires
significant domain expertise and struggles to generalize. Thus the field has
morphed into Molecular Property Prediction and been given over to learned
representations which are highly generalizable. The paper introduces fastprop,
a DeepQSPR framework which uses a cogent set of molecular level descriptors to
meet and exceed the performance of learned representations on diverse datasets
in dramatically less time. fastprop is freely available on github at
github.com/JacksonBurns/fastprop.
