给分子结构加可验证的数字签名，保护生成分子的版权。
MolMark: Safeguarding Molecular Structures through Learnable Atom-Level Watermarking
- 用学习型原子级水印，在不改变分子功能的前提下嵌入签名。
- 16位水印在旋转等变换下仍能95%准确提取，保留超90%关键属性。
- 可无缝接入生成模型，适合需要版权保护的药物研发场景。
基于AI的分子生成正在重塑药物发现与材料设计，但缺乏保护机制导致生成分子易被滥用且来源不明，影响科学可复现性与知识产权安全。为此，我们提出首个基于深度学习的分子水印框架MolMark，通过学习调节化学上有意义的原子级表示，并利用SE(3)不变特征实现几何鲁棒性，确保在旋转、平移和反射下仍保持水印有效性。MolMark可无缝集成至AI分子生成模型中，将水印视为可学习的转换操作，对分子结构干扰极小。在基准数据集QM9、GEOM-DRUG及先进生成模型GeoBFN、GeoLDM上的实验表明，该方法可嵌入16位水印，同时保留超过90%的关键分子性质，下游性能不受影响，且在SE(3)变换下水印提取准确率超过95%。MolMark为分子生成提供了可验证作者身份的可靠路径，支持可信、可问责的AI驱动分子发现。
原文摘要 · Abstract (English)
AI-driven molecular generation is reshaping drug discovery and materials design, yet the lack of protection mechanisms leaves AI-generated molecules vulnerable to unauthorized reuse and provenance ambiguity. Such limitation undermines both scientific reproducibility and intellectual property security. To address this challenge, we propose the first deep learning based watermarking framework for molecules (MolMark), which is exquisitely designed to embed high-fidelity digital signatures into molecules without compromising molecular functionalities. MolMark learns to modulate the chemically meaningful atom-level representations and enforce geometric robustness through SE(3)-invariant features, maintaining robustness under rotation, translation, and reflection. Additionally, MolMark integrates seamlessly with AI-based molecular generative models, enabling watermarking to be treated as a learned transformation with minimal interference to molecular structures. Experiments on benchmark datasets (QM9, GEOM-DRUG) and state-of-the-art molecular generative models (GeoBFN, GeoLDM) demonstrate that MolMark can embed 16-bit watermarks while retaining more than 90% of essential molecular properties, preserving downstream performance, and enabling >95% extraction accuracy under SE(3) transformations. MolMark establishes a principled pathway for unifying molecular generation with verifiable authorship, supporting trustworthy and accountable AI-driven molecular discovery.
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