Generative pre-trained Transformer (GPT) has demonstrates its great success
in natural language processing and related techniques have been adapted into
molecular modeling. Considering that text is the most important record for
scientific discovery, in this paper, we propose MolXPT, a unified language
model of text and molecules pre-trained on SMILES (a sequence representation of
molecules) wrapped by text. Briefly, we detect the molecule names in each
sequence and replace them to the corresponding SMILES. In this way, the SMILES
could leverage the information from surrounding text, and vice versa. The above
wrapped sequences, text sequences from PubMed and SMILES sequences from PubChem
are all fed into a language model for pre-training. Experimental results
demonstrate that MolXPT outperforms strong baselines of molecular property
prediction on MoleculeNet, performs comparably to the best model in
text-molecule translation while using less than half of its parameters, and
enables zero-shot molecular generation without finetuning.

本文提出了 MolXPT，一个基于 SMILES 的统一文本和分子预训练语言模型，将来自 PubMed 的文本序列和来自 PubChem 的 SMILES 序列一起进行预训练，并取得了超越强基线的性能。