Reliable Graph Neural Networks for Drug Discovery Under Distributional ShiftDownload PDF

09 Oct 2021, 14:49 (modified: 29 Nov 2021, 19:55)NeurIPS 2021 Workshop DistShift PosterReaders: Everyone
Keywords: Drug discovery, GNNs, Reliability, Overconfidence, Data shift
TL;DR: We introduce a new realistic benchmark and a distance-aware GNN model to improve GNN model robustness especially on calibration and overconfidence under data distributional shift.
Abstract: The concern of overconfident mispredictions under distributional shift demands extensive reliability research on Graph Neural Networks used in critical tasks in drug discovery. Here we first introduce CardioTox, a real-world benchmark on drug cardiotoxicity to facilitate such efforts. Our exploratory study shows overconfident mispredictions are often distant from training data. That leads us to develop distance-aware GNNs: GNN-SNGP. Through evaluation on CardioTox and three established benchmarks, we demonstrate GNN-SNGP's effectiveness in increasing distance-awareness, reducing overconfident mispredictions and making better calibrated predictions without sacrificing accuracy performance. Our ablation study further reveals the embeddings learned by GNN-SNGP improves distance-preservation over its base architecture and is one major factor for improvements. Arxiv link:
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