Graph Neural Network Powered Bayesian Optimization for Large Molecular Spaces

In silico screening is an essential component of drug and materials discovery. This is challenged by the increasingly intractable size of virtual libraries and the high cost of evaluating properties. We propose GNN-SS, a Graph Neural Network-powered Bayesian Optimization (BO) algorithm as a scalable solution. GNN-SS utilizes random sub-sampling to reduce the computational complexity of the BO problem, and diversifies queries for training the model. GNN-SS is sample-efficient, and rapidly narrows the search space by leveraging the generalization ability of GNNs. Our algorithm performs competitively on the QM9 dataset and achieves state-of-the-art performance on the PMO benchmark.