Graph Neural Network Powered Bayesian Optimization for Large Molecular Spaces

Miles Wang-Henderson, Bartu Soyuer, Parnian Kassraie, Andreas Krause, Ilija Bogunovic

Keywords: Bayesian Optimization, Drug Design, Graph Neural Networks, Virtual Screening

TL;DR: We propose a computationally light GNN-powered algorithm for BO on very large molecular spaces.

Abstract: *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 amongst screening algorithms on the PMO benchmark.

Submission Number: 116