用文字描述生成3D分子结构，支持复杂定制需求
Text-guided Diffusion Model for 3D Molecule Generation
- 通过文本引导的3D扩散模型生成分子
- 能准确理解文字描述并生成多样稳定分子
- 适合药物设计中需要精细描述的分子生成
从头生成具有特定性质的分子在生物学、化学和药物发现中至关重要。现有生成模型仅能以单一属性值为条件，难以处理由详细人类语言描述的复杂定制需求。为此，我们提出采用文本引导方式，引入TextSMOG——一种基于3D扩散模型的文本引导小分子生成方法，整合语言与扩散模型实现文本引导的小分子生成。该方法利用文本条件指导分子生成，提升了生成的稳定性和多样性。实验结果表明，TextSMOG能够有效捕捉并利用文本描述中的信息，成为响应复杂文本定制需求生成3D分子结构的强大工具。
原文摘要 · Abstract (English)
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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