Molecular representation learning is crucial for advancements in drug discovery and chemical analysis. Machine learning, particularly deep learning, has significantly impacted this field, with methods applied to various molecular applications such as property prediction, molecule generation, and optimization. However, creating comprehensive and effective molecular representations remains a challenge. Existing approaches often rely on string-based representations (SMILES, SELFIES) processed by RNNs or Transformers, or utilize graph neural networks (GNNs) which may ignore crucial chemical structural information embedded within molecular motifs. Self-supervised learning (SSL) frameworks have gained traction due to the abundance of unlabeled molecular data. This work aims to address the limitations of existing methods by proposing HiMol, which leverages hierarchical information within molecular graphs and employs multi-level self-supervised pre-training to learn more robust and informative molecular representations. The core challenge is to effectively capture and preserve the complete molecular structure, including crucial motifs, while integrating comprehensive information into the representations and designing effective self-supervised pretext tasks for pre-training.

Numerous methods leverage language models to process string-based molecular representations like SMILES and SELFIES, encoding them using RNNs (GRU, LSTM) or Transformers. Supervised learning is often used for property prediction. Self-supervised learning (SSL) has also been applied, with tasks like reconstructing original strings or recovering masked tokens. However, these methods often neglect the crucial topological information inherent in 2D molecular graphs. Consequently, many researchers have shifted their focus from 1D molecular strings to 2D graphs. Recent graph SSL-based pre-training frameworks attempt to capture the topology of 2D graphs, but often overlook the unique structural properties of molecules, such as rings and functional groups. Some works incorporate molecular graph characteristics, such as modeling clustering problems to learn molecular motifs or designing self-supervised pre-training frameworks for motif prediction. Other methods utilize graph augmentation to create different views for contrastive learning or consider chemical reaction relationships. Despite these advancements, challenges remain in adequately capturing molecular structure, fusing comprehensive information, and designing effective pretext tasks for self-supervised pre-training.

HiMol addresses these challenges with a two-component framework: Hierarchical Molecular Graph Neural Network (HMGNN) and Multi-level Self-supervised Pre-training (MSP). HMGNN encodes hierarchical molecular representations by first decomposing the molecular graph into motifs (substructures with chemical characteristics), which are then added as nodes to the graph. A graph-level node is also added to facilitate information interaction between different levels. The augmented graph, containing node-motif and motif-graph edges, is processed by GNNs to learn hierarchical representations (atom-level, motif-level, and graph-level). MSP designs multi-level pretext tasks: three generative tasks predict bond links, atom types, and bond types using atom representations; two predictive tasks predict the number of atoms and bonds using the graph-level representation. For downstream tasks, the graph-level representation is passed through a 2-layer MLP for property prediction. Pre-trained GNN weights are transferred, and the entire model is fine-tuned. The motif decomposition follows rules based on BRICS, avoiding the destruction of chemical characteristics. The graph-level node facilitates bidirectional information flow between local and global features. The model utilizes cross-entropy loss for atom-level tasks and smooth L1 loss for molecule-level tasks. The choice of GNN backbone was Graph Isomorphism Networks (GIN), with two different model scales (SMALL - 3 layers, LARGE - 5 layers) to investigate the effect of depth.

HiMol demonstrates superior performance in both classification and regression tasks compared to state-of-the-art methods on various datasets from MoleculeNet. In classification tasks (Table 1), HiMol achieves the best performance on four out of six datasets and competitive results on the remaining two. The average improvement over the best baseline is 2.4%. The larger model (HiMol LARGE) generally outperforms the smaller model (HiMol SMALL). The results highlight the importance of incorporating motif structure, as motif-based methods generally outperform other baselines. However, HiMol's approach of encoding motifs directly within the GNN backbone leads to superior performance. In regression tasks (Table 2), HiMol outperforms baselines on five out of six datasets, with a notable 55.5% MAE reduction on the challenging QM9 dataset. t-SNE visualizations (Figure 2 and Supplementary Figure 1) show that HiMol's learned representations effectively cluster molecules based on their properties. The visualizations also show that pre-training significantly improves representation quality compared to a model trained without pre-training (Supplementary Figure 2). Molecular representation similarity ranking (Figure 3 and Supplementary Figure 3) demonstrates HiMol's ability to capture chemical semantic information, identifying similar molecules based on structural and functional group similarities. Analysis of different fine-tuning strategies (Table 3 and Figure 4) indicates that full fine-tuning is superior to freezing the HMGNN parameters, showcasing the importance of the HMGNN architecture. The full HiMol model also shows faster convergence and greater stability during training compared to models without pre-training. Ablation studies (Figure 5) confirm the contribution of both HMGNN components (motif-level and graph-level nodes) and MSP’s multi-level self-supervised tasks.

HiMol's superior performance on both classification and regression tasks validates its effectiveness in learning informative molecular representations. The incorporation of hierarchical information (atom, motif, graph levels) within the HMGNN architecture and the design of multi-level self-supervised tasks in MSP are key contributors to its success. The results emphasize the importance of explicitly encoding motifs within the representation learning framework, rather than solely using them as pretext tasks. HiMol’s unique graph encoding approach, utilizing a graph-level node for bidirectional information flow between different levels, outperforms simpler approaches like graph pooling. The findings contribute to the broader understanding of molecular representation learning and highlight the potential of hierarchical models and multi-level self-supervised learning for improving the accuracy and robustness of molecular property prediction. The visualizations demonstrate that HiMol captures meaningful chemical semantics, supporting the claim that the learned representations capture relevant chemical information.

This research presents HiMol, a novel self-supervised learning framework for molecular property prediction that outperforms state-of-the-art methods. HiMol leverages hierarchical graph neural networks and a multi-level self-supervised pre-training strategy to learn informative and chemically meaningful molecular representations. Future work could explore the application of HiMol to larger and more diverse datasets, investigate alternative GNN architectures, and extend the framework to handle more complex molecular tasks such as reaction prediction and de novo drug design.

While HiMol demonstrates significant improvements, limitations exist. The performance is dependent on the quality of the motif decomposition method; improvements in motif identification could further enhance accuracy. The computational cost of pre-training might be high for extremely large datasets. The effectiveness of the model could also depend on the specific datasets used and the downstream tasks. Further investigation is needed to assess HiMol’s performance on datasets with different structural properties and chemical diversity.