AI RESEARCH
MM-DADM: Multimodal Drug-Aware Diffusion Model for Virtual Clinical Trials
arXiv CS.LG
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ArXi:2502.07297v3 Announce Type: replace 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 graphics 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.