Contextual Molecule Representation Learning from Chemical Reaction Knowledge
<|reference_start|>Contextual Molecule Representation Learning from Chemical Reaction Knowledge: In recent years, self-supervised learning has emerged as a powerful tool to harness abundant unlabelled data for representation learning and has been broadly adopted in diverse areas. However, when applied to molecular representation learning (MRL), prevailing techniques such as masked sub-unit reconstruction often fall short, due to the high degree of freedom in the possible combinations of atoms within molecules, which brings insurmountable complexity to the masking-reconstruction paradigm. To tackle this challenge, we introduce REMO, a self-supervised learning framework that takes advantage of well-defined atom-combination rules in common chemistry. Specifically, REMO pre-trains graph/Transformer encoders on 1.7 million known chemical reactions in the literature. We propose two pre-training objectives: Masked Reaction Centre Reconstruction (MRCR) and Reaction Centre Identification (RCI). REMO offers a novel solution to MRL by exploiting the underlying shared patterns in chemical reactions as \textit{context} for pre-training, which effectively infers meaningful representations of common chemistry knowledge. Such contextual representations can then be utilized to support diverse downstream molecular tasks with minimum finetuning, such as affinity prediction and drug-drug interaction prediction. Extensive experimental results on MoleculeACE, ACNet, drug-drug interaction (DDI), and reaction type classification show that across all tested downstream tasks, REMO outperforms the standard baseline of single-molecule masked modeling used in current MRL. Remarkably, REMO is the pioneering deep learning model surpassing fingerprint-based methods in activity cliff benchmarks.<|reference_end|>
@article{tang2024contextual,
    title={Contextual Molecule Representation Learning from Chemical Reaction
  Knowledge},
    author={Han Tang, Shikun Feng, Bicheng Lin, Yuyan Ni, JIngjing Liu, Wei-Ying
  Ma, Yanyan Lan},
    journal={arXiv preprint arXiv:2402.13779},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.13779},
    primaryClass={cs.LG cs.AI q-bio.BM}
}