Unlocking comprehensive molecular design across all scenarios with large language model and unordered chemical language.

ABSTRACT: Molecular generation stands at the forefront of AI-driven technologies, playing a crucial role in accelerating the development of small molecule drugs. The intricate nature of practical drug discovery necessitates the development of a versatile molecular generation framework that can tackle diverse drug design challenges. However, existing methodologies often struggle to encompass all aspects of small molecule drug design, particularly those rooted in language models, especially in tasks like linker design, due to the autoregressive nature of large language model-based approaches. To empower a language model for a wider range of molecular design tasks, we introduce an unordered simplified molecular-input line-entry system based on fragments (FU-SMILES). Building upon this foundation, we pr

SUBMITTER: Yue J 

PROVIDER: S-EPMC11352393 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature