ArticleBMC biology2025
Instruction multi-constraint molecular generation using a teacher-student large language model.
Article in BMC biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- AI-Driven Drug Discovery: Focus on Targets for Solid Tumors.Pharmaceutics · 2026Review
- Diffusion-based generative drug-like molecular editing with chemical natural language.Journal of pharmaceutical analysis · 2025Article
- A review of large language models and autonomous agents in chemistry.Chemical science · 2025Review
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14 authors.
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Abstract
backgroundWhile 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.
resultsWe 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 language across two-, three-, and four-constraint tasks, with an average molecular validity of over 99% and success ratio of 82.58%, 68.03%, and 67.48%, respectively. The model also exhibits adaptability through zero-shot testing, creating molecules that satisfy combinations of properties that have not been encountered. It can comprehend text inputs with various language styles, extending beyond the confines of outlined prompts.
conclusionsTSMMG presents an effective model for multi-constraint molecular generation using natural language. This framework is not only applicable to drug discovery but also serves as a reference for other related fields.
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