Text-guided Diffusion Model for 3D Molecule Generation

• URL: http://arxiv.org/abs/2410.03803v1
• Date: Fri, 4 Oct 2024 10:23:20 GMT
• Title: Text-guided Diffusion Model for 3D Molecule Generation
• Authors: Yanchen Luo, Junfeng Fang, Sihang Li, Zhiyuan Liu, Jiancan Wu, An Zhang, Wenjie Du, Xiang Wang,
• Abstract summary: We introduce TextSMOG, a new Text-guided Small Molecule Generation Approach via 3D Diffusion Model. 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.
• Score: 26.09786612721824
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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