Chemical Language Model Linker: blending text and molecules with modular adapters.

ABSTRACT: The development of large language models and multi-modal models has enabled the appealing idea of generating novel molecules from text descriptions. Generative modeling would shift the paradigm from relying on large-scale chemical screening to find molecules with desired properties to directly generating those molecules. However, multi-modal models combining text and molecules are often trained from scratch, without leveraging existing high-quality pretrained models. Training from scratch consumes more computational resources and prohibits model scaling. In contrast, we propose a lightweight adapter-based strategy named Chemical Language Model Linker (ChemLML). ChemLML blends the two single domain models and obtains conditional molecular generation from text des

SUBMITTER: Deng Y 

PROVIDER: S-EPMC12047907 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature