Molecule generation with desired properties has grown immensely in popularity
by disruptively changing the way scientists design molecular structures and
providing support for chemical and materials design. However, despite the
promising outcome, previous machine learning-based deep generative models
suffer from a reliance on complex, task-specific fine-tuning, limited
dimensional latent spaces, or the quality of expert rules. In this work, we
propose MolGen, a pre-trained molecular language model that effectively learns
and shares knowledge across multiple generation tasks and domains.
Specifically, we pre-train MolGen with the chemical language SELFIES on more
than 100 million unlabelled molecules. We further propose multi-task molecular
prefix tuning across several molecular generation tasks and different molecular
domains (synthetic & natural products) with a self-feedback mechanism.
Extensive experiments show that MolGen can obtain superior performances on
well-known molecular generation benchmark datasets. The further analysis
illustrates that MolGen can accurately capture the distribution of molecules,
implicitly learn their structural characteristics, and efficiently explore the
chemical space with the guidance of multi-task molecular prefix tuning. Codes,
datasets, and the pre-trained model will be available in
this https URL.

本文提出了一个经过预训练的分子语言模型 MolGen，通过多任务分子前缀调整自我反馈机制，可以在多个分子生成任务和不同领域（合成和天然产物）中有效地学习和共享知识，进而在众多分子生成基准数据集上获得优越性能。