Enhancing Molecular Property Predictions by Learning from Bond Modelling and Interactions

TL;DR

DeMol is introduced, a dual-graph framework whose architecture is motivated by a rigorous information-theoretic analysis demonstrating the information gain from a bond-centric perspective, and confirms the superiority of explicitly modelling bond information and interactions.

Abstract

Molecule representation learning is crucial for understanding and predicting molecular properties. However, conventional atom-centric models, which treat chemical bonds merely as pairwise interactions, often overlook complex bond-level phenomena like resonance and stereoselectivity. This oversight limits their predictive accuracy for nuanced chemical behaviors. To address this limitation, we introduce \textbf{DeMol}, a dual-graph framework whose architecture is motivated by a rigorous information-theoretic analysis demonstrating the information gain from a bond-centric perspective. DeMol explicitly models molecules through parallel atom-centric and bond-centric channels. These are synergistically fused by multi-scale Double-Helix Blocks designed to learn intricate atom-atom, atom-bond, and bond-bond interactions. The framework's geometric consistency is further enhanced by a regularization term based on covalent radii to enforce chemically plausible structures. Comprehensive evaluations on diverse benchmarks, including PCQM4Mv2, OC20 IS2RE, QM9, and MoleculeNet, show that DeMol establishes a new state-of-the-art, outperforming existing methods. These results confirm the superiority of explicitly modelling bond information and interactions, paving the way for more robust and accurate molecular machine learning.

Figures (7)

• Figure 1: An example of the different bonds within benzene (C$_6$H$_6$). Formed by $p$-orbital overlap, the delocalized $\pi$ system creates electron density above/below the ring plane, providing stability and unique reactivity.
• Figure 2: An example of bond-bond interactions in molecules that affect properties. Left is the anticancer drug cisplatin, where two ammonia ligands and two chloride ions bind to a central platinum atom in a cis configuration. Right is transplatin, which possesses the same atomic composition but features ligands in a trans configuration, rendering it pharmacologically ineffective.
• Figure 3: DeMol integrates atom-centric and bond-centric channels via dual-graph representations. Cross-level (atom-bond) interactions are enforced through double-helix blocks, ensuring geometric consistency.
• Figure 4: Results on molecular property classification tasks. The table version is Appendix Table \ref{['tab:MoleculeNet']}.
• Figure 5: Visualisation on self-attention map of multi-heads independently.