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Workshop: Adaptive Experimental Design and Active Learning in the Real World

Graph Neural Bayesian Optimization for Virtual Screening

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


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.

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