MOLAR: Learning Multimodal Molecular Representations from Noisy Labels

2026-06-16

AI Summary

Molecular property prediction is often compromised by label noise arising from experimental errors, database inconsistencies, or weak annotations, leading to biased model learning. To address this challenge, this work proposes MOLAR, a novel framework that explicitly disentangles latent clean properties from observed noisy labels. MOLAR leverages graph neural networks and text encoders to extract multimodal residual evidence and incorporates a class-aware noise channel to jointly infer label reliability and modality-specific confidence. Evaluated on both real-world noisy datasets and benchmarks with synthetically flipped labels, MOLAR consistently outperforms existing baselines. Visualization analyses further demonstrate its capability to effectively diagnose label quality and modality contributions, thereby enhancing model robustness and interpretability.

Abstract

Motivation: Noisy labels are a common challenge in molecular property prediction because molecular annotations are often obtained from assays, curated databases, or weak annotation pipelines rather than directly observed clean biological states. Treating recorded labels as reliable supervision can cause models to memorize corrupted observations and learn misleading molecular evidence. In multimodal molecular representation learning, this issue can be amplified by graph-text fusion or alignment, which may propagate label-induced errors across modalities. Results: We propose MOLAR, a noise-aware framework for learning multimodal molecular representations from noisy labels. MOLAR separates latent clean-property inference from recorded-label observation: graph and text views contribute residual evidence to a clean-property distribution, and a categorical label-observation channel maps this distribution to recorded labels for training. This formulation derives posterior label reliability and modality-specific molecular evidence from the model. Experiments on naturally noisy molecular benchmarks and controlled label-flipping benchmarks show that MOLAR consistently outperforms representative baselines. Visualization analyses further show that MOLAR provides interpretable reliability and modality-evidence diagnostics.

Problem

• noisy labels
• molecular property prediction
• multimodal molecular representations
• label noise
• molecular representation learning

Innovation

• noisy labels
• multimodal molecular representation
• graph-text fusion
• label reliability
• residual evidence