Evaluating Self-Supervised Learning for Molecular Graph Embeddings
Graph Self-Supervised Learning (GSSL) provides a robust pathway for acquiring embeddings without expert labelling, a capability that carries profound implications for molecular graphs due to the staggering number of potential molecules and the high cost of obtaining labels. However, GSSL methods are designed not for optimisation within a specific domain but rather for transferability across a variety of downstream tasks. This broad applicability complicates their evaluation. Addressing this challenge, we present "Molecular Graph Representation Evaluation" (MOLGRAPHEVAL), generating detailed profiles of molecular graph embeddings with interpretable and diversified attributes. MOLGRAPHEVAL offers a suite of probing tasks grouped into three categories: (i) generic graph, (ii) molecular substructure, and (iii) embedding space properties. By leveraging MOLGRAPHEVAL to benchmark existing GSSL methods against both current downstream datasets and our suite of tasks, we uncover significant inconsistencies between inferences drawn solely from existing datasets and those derived from more nuanced probing. These findings suggest that current evaluation methodologies fail to capture the entirety of the landscape.
Code (1)
Tasks
Self-Supervised LearningSimilar Papers 제목 키워드 기반
Hierarchical Molecular Representation Learning via Fragment-Based Self-Supervised Embedding Prediction
Graph self-supervised learning (GSSL) has demonstrated strong potential for generating expressive graph embeddings without the need for human annotations, making it particularly valuable in domains with high labeling cos…
Molecular Property PredictionSelf-Supervised LearningRepresentation LearningTransfer LearningLearning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining
High-quality molecular representations are essential for property prediction and molecular design, yet large labeled datasets remain scarce. While self-supervised pretraining on molecular graphs has shown promise, many e…
Representation LearningGraph Neural NetworkKnowledge-aware Contrastive Molecular Graph Learning
Leveraging domain knowledge including fingerprints and functional groups in molecular representation learning is crucial for chemical property prediction and drug discovery. When modeling the relation between graph struc…
Contrastive LearningDrug DiscoveryGraph Learningmolecular representation+3Motif-based Graph Self-Supervised Learning for Molecular Property Prediction
Predicting molecular properties with data-driven methods has drawn much attention in recent years. Particularly, Graph Neural Networks (GNNs) have demonstrated remarkable success in various molecular generation and predi…
Molecular Property PredictionProperty PredictionRetrosynthesisSelf-Supervised LearningLarge-Scale Chemical Language Representations Capture Molecular Structure and Properties
Models based on machine learning can enable accurate and fast molecular property predictions, which is of interest in drug discovery and material design. Various supervised machine learning models have demonstrated promi…
Drug DiscoveryMolecular Property Predictionmolecular representationRepresentation Learning