统一引导框架让分子生成模型灵活控制几何结构，无需重新训练。
Unified Guidance for Geometry-Conditioned Molecular Generation
- 用统一框架在推理时灵活控制分子几何形状，不需额外训练
- 在基于结构、片段和配体的药物设计中表现媲美甚至优于专用模型
- 适合希望快速适配多种药物设计场景的研究者使用
有效设计分子几何结构对推动制药创新至关重要，该领域因生成模型的成功而备受关注，尤其是扩散模型。然而，当前的分子扩散模型通常针对特定下游任务设计，缺乏适应性。我们提出UniGuide，一种用于无条件扩散模型的几何控制统一引导框架，可在推理阶段灵活施加条件，无需额外训练或网络结构。我们展示了如何将基于结构、基于片段和基于配体的药物设计任务纳入UniGuide框架，并在实验中表现出与专用模型相当或更优的性能。该框架更具通用性，有望简化分子生成模型的开发，使其能便捷应用于多样化的应用场景。
原文摘要 · Abstract (English)
Effectively designing molecular geometries is essential to advancing pharmaceutical innovations, a domain, which has experienced great attention through the success of generative models and, in particular, diffusion models. However, current molecular diffusion models are tailored towards a specific downstream task and lack adaptability. We introduce UniGuide, a framework for controlled geometric guidance of unconditional diffusion models that allows flexible conditioning during inference without the requirement of extra training or networks. We show how applications such as structure-based, fragment-based, and ligand-based drug design are formulated in the UniGuide framework and demonstrate on-par or superior performance compared to specialised models. Offering a more versatile approach, UniGuide has the potential to streamline the development of molecular generative models, allowing them to be readily used in diverse application scenarios.
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