Work

Text-guided Diffusion Model for 3D Molecule Generation

Year: 2024

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2410.03803

Authors: Luo, Yanchen, Fang, Junfeng, Li, Sihang, Liu, Zhiyuan, Wu, Jiancan, Zhang, An, Du, Wenjie, Wang, Xiang

Institutions: University of Science and Technology of China, National University of Singapore

Language: English

Topic: Computational Drug Discovery Methods

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Natural Science Foundation of China, University of Science and Technology of China

Award: G7890592427

OpenAlex ID: W4403928945

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