MolMark: Safeguarding Molecular Structures through Learnable Atom-Level Watermarking

TL;DR

MolMark introduces a deep learning framework that embeds robust, high-fidelity watermarks into molecules at the atom level, safeguarding intellectual property without compromising molecular properties or generative performance.

Contribution

This work is the first to develop a learnable, geometry-aware watermarking method for molecules that maintains molecular integrity and integrates seamlessly with AI generative models.

Findings

Successfully embeds 16-bit watermarks with over 90% property retention.

Achieves over 95% watermark extraction accuracy under SE(3) transformations.

Demonstrates robustness across benchmark datasets and generative models.

Abstract

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.