Chemical Language Model Linker: Blending Text and Molecules with Modular Adapters.

ABSTRACT: The development of large language models and multimodal 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, multimodal 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 descr

SUBMITTER: Deng Y 

PROVIDER: S-EPMC12421661 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature