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

2026-01-16

AI Summary

This work addresses the challenge of leveraging functional group–based causal priors and identifying critical substructures in few-shot molecular property prediction. To this end, we propose CaMol, a novel framework that introduces causal inference into this task for the first time. By constructing a contextual graph that integrates functional groups, molecular structures, and target properties, and combining it with a learnable atomic masking strategy and a chemistry-informed backdoor adjustment mechanism, CaMol effectively disentangles causal effects from confounding factors to identify substructures directly causally linked to the target property. Extensive experiments demonstrate that CaMol significantly improves both prediction accuracy and sample efficiency across multiple datasets. Moreover, the identified causal substructures show strong alignment with known functional groups, highlighting the model’s high performance and interpretability.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• few-shot molecular property prediction
• causal substructures
• functional groups
• context-aware graph learning
• molecular property prediction

Innovation

• context-aware graph
• causal inference
• few-shot learning
• molecular property prediction
• functional groups