Graph Neural Bayesian Optimization for Virtual Screening

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

Keywords: Molecular Optimization

TL;DR: We introduce GNN-SS, a computationally light algorithm that leverages random sub-sampling of data and features, achieving state-of-the-art amongst screening methods in the Practical Molecular Optimization (PMO) benchmark

Abstract: Virtual screening is an essential component of early-stage 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 (GNN) powered Bayesian Optimization (BO) algorithm. GNN-SS utilizes random sub-sampling to reduce the computational complexity of the BO problem, and diversifies queries for training the model. We further introduce data-independent projections to efficiently model second-order random feature interactions, and improve uncertainty estimates. GNN-SS is computationally light, sample-efficient, and rapidly narrows the search space by leveraging the generalization ability of GNNs. Our algorithm achieves state-of-the-art performance among screening methods for the Practical Molecular Optimization benchmark.

Submission Number: 29