# Gaussian Process Molecule Property Prediction with FlowMO

arXiv: https://arxiv.org/abs/2010.01118 · HTML: https://mlsanity.com/paper/2010.01118/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2020.

## Built on by (most cited first, 18 of 18)

- [GFlowNets for AI-Driven Scientific Discovery](https://mlsanity.com/paper/2302.00615/) (2023)
- [High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning](https://mlsanity.com/paper/2106.03609/) (2021)
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- [Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces](https://mlsanity.com/paper/2111.01186/) (2021)
- [GAUCHE: A Library for Gaussian Processes in Chemistry](https://mlsanity.com/paper/2212.04450/) (2022)
- [Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation](https://mlsanity.com/paper/2301.10123/) (2023)
- [Assigning Confidence to Molecular Property Prediction](https://mlsanity.com/paper/2102.11439/) (2021)
- [Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spaces](https://mlsanity.com/paper/2507.03910/) (2025)
- [Calibration and generalizability of probabilistic models on low-data chemical datasets with DIONYSUS](https://mlsanity.com/paper/2212.01574/) (2022)
- [Be aware of overfitting by hyperparameter optimization!](https://mlsanity.com/paper/2407.20786/) (2024)
- [Ranking over Regression for Bayesian Optimization and Molecule Selection](https://mlsanity.com/paper/2410.09290/) (2024)
- [A Gaussian process model for chemoinformatics with application to the hazard classification of organic solvents](https://mlsanity.com/paper/2405.09989/) (2024)
- [Hash Collisions in Molecular Fingerprints: Effects on Property Prediction and Bayesian Optimization](https://mlsanity.com/paper/2511.17078/) (2025)
- [Applications of Gaussian Processes at Extreme Lengthscales: From Molecules to Black Holes](https://mlsanity.com/paper/2303.14291/) (2023)
- [Simulation Based Bayesian Optimization](https://mlsanity.com/paper/2401.10811/) (2024)
- [Representing Molecules with Algebraic Data Types: Beyond SMILES and SELFIES](https://mlsanity.com/paper/2501.13633/) (2025)
- [A decoupled alignment kernel for peptide membrane permeability predictions](https://mlsanity.com/paper/2511.21566/) (2025)
- [Triply-Scalable Equivariant Gaussian Process Modeling](https://mlsanity.com/paper/2609.21085/) (2026)

## Abstract (arXiv)

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.