[2502.07297] MM-DADM: Multimodal Drug-Aware Diffusion Model for Virtual Clinical Trials

Computer Science > Machine Learning arXiv:2502.07297 (cs) [Submitted on 11 Feb 2025 (v1), last revised 30 Mar 2026 (this version, v3)] Title:MM-DADM: Multimodal Drug-Aware Diffusion Model for Virtual Clinical Trials Authors:Qian Shao, Bang Du, Zepeng Li, Qiyuan Chen, Jiahe Chen, Hongxia Xu, Jimeng Sun, Jian Wu, Jintai Chen View a PDF of the paper titled MM-DADM: Multimodal Drug-Aware Diffusion Model for Virtual Clinical Trials, by Qian Shao and 8 other authors View PDF HTML (experimental) Abstract:High failure rates in cardiac drug development necessitate virtual clinical trials via electrocardiogram (ECG) generation to reduce risks and costs. However, existing ECG generation models struggle to balance morphological realism with pathological flexibility, fail to disentangle demographics from genuine drug effects, and are severely bottlenecked by early-phase data scarcity. To overcome these hurdles, we propose the Multimodal Drug-Aware Diffusion Model (MM-DADM), the first generative framework for generating individualized drug-induced ECGs. Specifically, our proposed MM-DADM integrates a Dynamic Cross-Attention (DCA) module that adaptively fuses External Physical Knowledge (EPK) to preserve morphological realism while avoiding the suppression of complex pathological nuances. To resolve feature entanglement, a Causal Feature Encoder (CFE) actively filters out demographic noise to extract pure pharmacological representations. These representations subsequently guide a Causal-...