Optimizing OOD Detection in Molecular Graphs: A Novel Approach with Diffusion Models

TL;DR

This paper introduces PGR-MOOD, a novel, scalable diffusion model-based framework for molecular OOD detection that improves accuracy by using prototypical graphs and advanced similarity metrics, addressing key challenges in efficiency and structural complexity.

Contribution

The paper proposes PGR-MOOD, a new approach combining prototypical graph reconstruction and efficient similarity metrics for improved molecular OOD detection.

Findings

Outperforms six baselines on ten benchmark datasets.

Achieves higher detection accuracy with reduced computational time.

Effectively distinguishes ID and OOD molecules using prototypical graphs.

Abstract

The open-world test dataset is often mixed with out-of-distribution (OOD) samples, where the deployed models will struggle to make accurate predictions. Traditional detection methods need to trade off OOD detection and in-distribution (ID) classification performance since they share the same representation learning model. In this work, we propose to detect OOD molecules by adopting an auxiliary diffusion model-based framework, which compares similarities between input molecules and reconstructed graphs. Due to the generative bias towards reconstructing ID training samples, the similarity scores of OOD molecules will be much lower to facilitate detection. Although it is conceptually simple, extending this vanilla framework to practical detection applications is still limited by two significant challenges. First, the popular similarity metrics based on Euclidian distance fail to consider the complex graph structure. Second, the generative model involving iterative denoising steps is time-consuming especially when it runs on the enormous pool of drugs. To address these challenges, our research pioneers an approach of Prototypical Graph Reconstruction for Molecular OOD Detection, dubbed as PGR-MOOD and hinges on three innovations: i) An effective metric to comprehensively quantify the matching degree of input and reconstructed molecules; ii) A creative graph generator to construct prototypical graphs that are in line with ID but away from OOD; iii) An efficient and scalable OOD detector to compare the similarity between test samples and pre-constructed prototypical graphs and omit the generative process on every new molecule. Extensive experiments on ten benchmark datasets and six baselines are conducted to demonstrate our superiority.