CMCM-DLM：用扩散语言模型实现跨模态可控分子生成

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Abstract:The increasing variety of molecular data creates a need for generative models that can flexibly incorporate heterogeneous constraints across modalities. However, existing SMILES-based diffusion models are typically designed for a fixed conditioning modality, and introducing new constraints often requires retraining the model. To address this limitation, we propose Cross-Modality Controlled Molecule Generation with Diffusion Language Model (CMCM-DLM), a modular framework that extends a pre-trained diffusion model to support heterogeneous molecular constraints without retraining the backbone. We demonstrate CMCM-DLM using two complementary modalities: molecular structure and chemical properties. Specifically, a Structure Control Module (SCM) guides early diffusion steps to establish the molecular scaffold, while a Property Control Module (PCM) subsequently steers generation toward target chemical properties. This staged design enables flexible integration of different molecular constraints within a unified generative framework. Experiments on multiple datasets demonstrate effective cross-modal controllability and strong adaptability, highlighting the potential of CMCM-DLM for heterogeneous molecular data modeling and data-driven drug discovery.

Comments:	Revised manuscript with updated references
Subjects:	Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as:	arXiv:2508.14748 [cs.LG]
	(or arXiv:2508.14748v2 [cs.LG] for this version)
	https://doi.org/10.48550/arXiv.2508.14748 arXiv-issued DOI via DataCite

Submission history

From: Yunzhe Zhang [view email]
[v1] Wed, 20 Aug 2025 14:48:44 UTC (1,085 KB)
[v2] Tue, 6 Oct 2026 02:59:10 UTC (2,220 KB)