Instruction multi-constraint molecular generation using a teacher-student large language model.

ABSTRACT:

Background

While various models and computational tools have been proposed for structure and property analysis of molecules, generating molecules that conform to all desired structures and properties remains a challenge.

Results

We introduce a multi-constraint molecular generation large language model, TSMMG, which, akin to a student, incorporates knowledge from various small models and tools, namely, the "teachers." To train TSMMG, we construct a large set of text-molecule pairs by extracting molecular knowledge from these "teachers," enabling it to generate novel molecules that conform to the descriptions through various text prompts. We experimentally show that TSMMG remarkably performs in generating molecules that meet complex property requirements described in natural l

SUBMITTER: Zhou P 

PROVIDER: S-EPMC12020078 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature