Context-aware Graph Causality Inference for Few-Shot Molecular Property Prediction

• URL: http://arxiv.org/abs/2601.11135v1
• Date: Fri, 16 Jan 2026 09:49:50 GMT
• Title: Context-aware Graph Causality Inference for Few-Shot Molecular Property Prediction
• Abstract summary: We propose CaMol, a context-aware graph causality inference framework.<n>First, we introduce a context graph that encodes chemical knowledge by linking functional groups, molecules, and properties.<n>Second, we propose a learnable atom masking strategy to disentangle causal substructures from confounding ones.<n>Third, we introduce a distribution intervener that applies backdoor adjustment by combining causal substructures with chemically grounded confounders.
• Score: 8.42839603549236
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Molecular property prediction is becoming one of the major applications of graph learning in Web-based services, e.g., online protein structure prediction and drug discovery. A key challenge arises in few-shot scenarios, where only a few labeled molecules are available for predicting unseen properties. Recently, several studies have used in-context learning to capture relationships among molecules and properties, but they face two limitations in: (1) exploiting prior knowledge of functional groups that are causally linked to properties and (2) identifying key substructures directly correlated with properties. We propose CaMol, a context-aware graph causality inference framework, to address these challenges by using a causal inference perspective, assuming that each molecule consists of a latent causal structure that determines a specific property. First, we introduce a context graph that encodes chemical knowledge by linking functional groups, molecules, and properties to guide the discovery of causal substructures. Second, we propose a learnable atom masking strategy to disentangle causal substructures from confounding ones. Third, we introduce a distribution intervener that applies backdoor adjustment by combining causal substructures with chemically grounded confounders, disentangling causal effects from real-world chemical variations. Experiments on diverse molecular datasets showed that CaMol achieved superior accuracy and sample efficiency in few-shot tasks, showing its generalizability to unseen properties. Also, the discovered causal substructures were strongly aligned with chemical knowledge about functional groups, supporting the model interpretability.

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