Gaussian Process Molecule Property Prediction with FlowMO

• URL: http://arxiv.org/abs/2010.01118v2
• Date: Wed, 14 Oct 2020 08:23:55 GMT
• Title: Gaussian Process Molecule Property Prediction with FlowMO
• Authors: Henry B. Moss, Ryan-Rhys Griffiths
• Abstract summary: FlowMO is an open-source library for molecular property prediction with Gaussian Processes. It enables the user to make predictions with well-calibrated uncertainty estimates, an output central to active learning and molecular design applications.
• Score: 7.72630981555675
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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