Text-guided Diffusion Model for 3D Molecule Generation

The de novo generation of molecules with targeted properties is crucial in biology, chemistry, and drug discovery. Current generative models are limited to using single property values as conditions, struggling with complex customizations described in detailed human language. To address this, we pro...

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Bibliographic Details
Main Authors Luo, Yanchen, Fang, Junfeng, Li, Sihang, Liu, Zhiyuan, Wu, Jiancan, Zhang, An, Du, Wenjie, Wang, Xiang
Format Journal Article
LanguageEnglish
Published 04.10.2024
Subjects
Computer Science - Artificial Intelligence
Computer Science - Learning
Physics - Chemical Physics
Quantitative Biology - Biomolecules
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Summary:The de novo generation of molecules with targeted properties is crucial in biology, chemistry, and drug discovery. Current generative models are limited to using single property values as conditions, struggling with complex customizations described in detailed human language. To address this, we propose the text guidance instead, and introduce TextSMOG, a new Text-guided Small Molecule Generation Approach via 3D Diffusion Model which integrates language and diffusion models for text-guided small molecule generation. This method uses textual conditions to guide molecule generation, enhancing both stability and diversity. Experimental results show TextSMOG's proficiency in capturing and utilizing information from textual descriptions, making it a powerful tool for generating 3D molecular structures in response to complex textual customizations.
DOI:10.48550/arxiv.2410.03803