Molecular representation learning plays an essential role in cheminformatics.
Recently, language model-based approaches have gained popularity as an
alternative to traditional expert-designed features to encode molecules.
However, these approaches only utilize a single molecular language for
representation learning. Motivated by the fact that a given molecule can be
described using different languages such as Simplified Molecular Line Entry
System (SMILES), The International Union of Pure and Applied Chemistry (IUPAC),
and The IUPAC International Chemical Identifier (InChI), we propose a
multilingual molecular embedding generation approach called MM-Deacon
(multilingual molecular domain embedding analysis via contrastive learning).
MM-Deacon is pre-trained using SMILES and IUPAC as two different languages on
large-scale molecules. We evaluated the robustness of our method on seven
molecular property prediction tasks from MoleculeNet benchmark, zero-shot
cross-lingual retrieval, and a drug-drug interaction prediction task.

该研究提出了一种基于对比学习构建多语言分子嵌入的预训练方法 MM-Deacon，通过对 SMILES 和 IUPAC 语言的大规模分子进行预训练，在分子性质预测、零样本跨语言检索和药物相互作用预测任务中取得了鲁棒性良好的结果。