Self-Supervised Learning for Molecular Property Prediction: Methods, Multimodal Insights, and Benchmark Comparisons

Area of Science:

• Cheminformatics
• Computational Chemistry
• Artificial Intelligence in Drug Discovery

Background:

• Accurate molecular property prediction is crucial for accelerating computer-aided drug discovery and identifying potential therapeutics.
• Self-supervised learning (SSL) leverages large unlabeled molecular datasets to learn effective representations for cheminformatics.
• Integrating chemical domain knowledge enhances SSL models' ability to capture molecular characteristics.

Purpose of the Study:

• To provide a systematic review of recent advancements in self-supervised learning for molecular property prediction.
• To analyze the impact of multimodal molecular representation learning on prediction accuracy and expressiveness.
• To examine the synergy between multimodal modeling and SSL, particularly in low-label data scenarios.

Main Methods:

• Systematic review of recent methodological developments in SSL-based molecular property prediction.
• Analysis of multimodal representation learning integrating sequence, graph, 3D structure, and textual data.
• Comparison of multimodal SSL models against conventional SSL models on benchmark tasks.

Main Results:

• Multimodal molecular representation learning significantly improves the quality and expressiveness of learned representations.
• Complementary data modalities enhance SSL performance in low-label settings.
• Demonstrated practical benefits of multimodal properties on downstream prediction tasks.

Conclusions:

• Multimodal SSL frameworks offer a promising direction for robust and generalizable molecular property prediction.
• Addressing challenges like 3D data scarcity and modality imbalance is key for future development.
• Future research should focus on creating more interpretable and biologically meaningful models.