@inproceedings{
wang-henderson2023graph,
title={Graph Neural Bayesian Optimization for Virtual Screening},
author={Miles Wang-Henderson and Bartu Soyuer and Parnian Kassraie and Andreas Krause and Ilija Bogunovic},
booktitle={NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning in the Real World},
year={2023},
url={https://openreview.net/forum?id=t3PzfH98Mq}
}
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.
Graph Neural Bayesian Optimization for Virtual Screening
Miles Wang-Henderson Bartu Soyuer Parnian Kassraie Andreas Krause Ilija Bogunovic