Generalizable, Fast, and Accurate DeepQSPR with fastprop
This addresses the problem of efficient and accurate molecular property prediction for researchers in cheminformatics, offering a fast and generalizable alternative to existing methods.
The paper tackles the challenge of mapping molecular structure to properties by introducing fastprop, a DeepQSPR framework that uses molecular descriptors to outperform learned representations on diverse datasets with significantly reduced computational time.
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.