How Creative Are Large Language Models in Generating Molecules?

2026-04-20

AI Summary

This work addresses the challenge of molecular generation under multiple constraints—including physicochemical properties, ADMET profiles, and biological activity—within the vast chemical space, where the creative capabilities of large language models (LLMs) remain insufficiently characterized. The study introduces the concept of “functional creativity” and proposes a novel two-dimensional evaluation framework encompassing both convergent and divergent creativity specifically tailored for LLM-based molecular design. By leveraging natural language prompts to guide molecule generation, the authors conduct empirical analyses across multidimensional constraint tasks. Their findings reveal that LLMs not only enhance constraint satisfaction under stringent conditions but also exhibit discernible patterns of functional creativity, thereby offering both theoretical grounding and practical guidance for the judicious application of LLMs in drug discovery.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Natural Language Processing: Generation

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

Molecule generation requires satisfying multiple chemical and biological constraints while searching a large and structured chemical space. This makes it a non-binary problem, where effective models must identify non-obvious solutions under constraints while maintaining exploration to improve success by escaping local optima. From this perspective, creativity is a functional requirement in molecular generation rather than an aesthetic notion. Large language models (LLMs) can generate molecular representations directly from natural language prompts, but it remains unclear what type of creativity they exhibit in this setting and how it should be evaluated. In this work, we study the creative behavior of LLMs in molecular generation through a systematic empirical evaluation across physicochemical, ADMET, and biological activity tasks. We characterize creativity along two complementary dimensions, convergent creativity and divergent creativity, and analyze how different factors shape these behaviors. Our results indicate that LLMs exhibit distinct patterns of creative behavior in molecule generation, such as an increase in constraint satisfaction when additional constraints are imposed. Overall, our work is the first to reframe the abilities required for molecule generation as creativity, providing a systematic understanding of creativity in LLM-based molecular generation and clarifying the appropriate use of LLMs in molecular discovery pipelines.

Problem

• molecule generation
• creativity
• large language models
• chemical space
• constraint satisfaction

Innovation

• creativity
• large language models
• molecule generation
• convergent creativity
• divergent creativity