Gaussian Process Molecule Property Prediction with FlowMO
arXiv:2010.01118
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.
References in corpus (10)
- Semi-Supervised Classification with Graph Convolutional Networks
- Neural Message Passing for Quantum Chemistry
- Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks
- Gaussian Processes for Big Data
- Batch Bayesian Optimization via Local Penalization
- Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning
- ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations
- Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining
- A benchmark study on reliable molecular supervised learning via Bayesian learning
- BOSH: Bayesian Optimization by Sampling Hierarchically
Cited by in corpus (8)
- GFlowNets for AI-Driven Scientific Discovery
- Are we Forgetting about Compositional Optimisers in Bayesian Optimisation?
- High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning
- Achieving Robustness to Aleatoric Uncertainty with Heteroscedastic Bayesian Optimisation
- GIBBON: General-purpose Information-Based Bayesian OptimisatioN
- Ranking over Regression for Bayesian Optimization and Molecule Selection
- Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces
- Scalable Thompson Sampling using Sparse Gaussian Process Models