用因果注意力机制实现精准文本驱动分子生成
Mol-CADiff: Causality-Aware Autoregressive Diffusion for Molecule Generation
- 基于扩散模型，引入因果注意力捕捉文本与分子结构的因果关系
- 生成分子在多样性、新颖性和化学有效性上均优于现有方法
- 适合需要语言描述驱动药物分子设计的研究者
新型分子的设计是药物发现和材料科学中的关键挑战。传统方法依赖试错，而近期深度学习方法虽加速了分子生成，但现有模型在基于特定文本描述生成分子方面仍存在困难。我们提出 Mol-CADiff，一种基于扩散的框架，利用因果注意力机制实现文本条件下的分子生成。该方法显式建模文本提示与分子结构之间的因果关系，增强模态内与跨模态的依赖建模能力，从而实现对生成过程的精确控制。大量实验表明，Mol-CADiff 在生成多样、新颖且化学有效的分子方面优于当前最优方法，且更符合指定属性要求，支持更直观的语言驱动分子设计。
原文摘要 · Abstract (English)
The design of novel molecules with desired properties is a key challenge in drug discovery and materials science. Traditional methods rely on trial-and-error, while recent deep learning approaches have accelerated molecular generation. However, existing models struggle with generating molecules based on specific textual descriptions. We introduce Mol-CADiff, a novel diffusion-based framework that uses causal attention mechanisms for text-conditional molecular generation. Our approach explicitly models the causal relationship between textual prompts and molecular structures, overcoming key limitations in existing methods. We enhance dependency modeling both within and across modalities, enabling precise control over the generation process. Our extensive experiments demonstrate that Mol-CADiff outperforms state-of-the-art methods in generating diverse, novel, and chemically valid molecules, with better alignment to specified properties, enabling more intuitive language-driven molecular design.
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