We present FlowMO: an open-source Python library for molecular property prediction with Gaussian Processes. Built upon GPflow and RDKit, FlowMO enables the user to make predictions with well-calibrated uncertainty estimates, an output central to active learning and molecular design applications. Gaussian Processes are particularly attractive for modelling small molecular datasets, a characteristic of many real-world virtual screening campaigns where high-quality experimental data is scarce. Computational experiments across three small datasets demonstrate comparable predictive performance to deep learning methods but with superior uncertainty calibration.
@article{arxiv.2010.01118,
title = {Gaussian Process Molecule Property Prediction with FlowMO},
author = {Henry B. Moss and Ryan-Rhys Griffiths},
journal= {arXiv preprint arXiv:2010.01118},
year = {2020}
}