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◆ Archives of toxicology2026-08-11

Cardiosim-Tox: an interpretable multitask deep learning QSAR platform with multimodal feature fusion for predicting hERG, Cav1.2, and Nav1.5 blockade risk and potency.

Fauzan Syarif Nursyafi, Byunggyu Kang, Junhyeok Eom, Rahmafatin Nurul Izza, Abdul Latif Fauzan, Nadilla Hafani Putri, Ulfa Latifa Hanum, Yossi Olivia, Sanjida Afrin Rose, Ki Moo Lim

原始摘要(英文原文)· Original abstract
Drug-induced cardiotoxicity, mainly driven by cardiac ion-channel blockade, remains a leading cause of drug attrition and post-market withdrawal, highlighting the need for reliable early-stage screening tools. Existing computational methods, including QSAR models, largely focus on single ion channels, limiting their ability to assess multi-channel safety profiles. To address this gap, we developed Cardiosim-Tox, a modular multi-modal deep learning platform to simultaneously predicts blockade risk (binary classification) and potency (pIC50) for hERG, Cav1.2, and Nav1.5. The framework integrates topological fingerprints, Mordred 2D/3D descriptors, and molecular graph encodings under both single-task (STL) and multi-task learning (MTL) configurations. Developed on 31,816 curated unique compounds, model performance was evaluated on three Tanimoto similarity-stratified external validation sets, with domain-shift metrics and applicability domain analyses supporting robustness. MTL models consistently outperformed STL, while the full modality combination (FP + MD + Graph) achieved AUCs of 0.93-0.98 for Cav1.2, 0.88-0.96 for Nav1.5, and 0.79-0.95 for hERG across validation sets. Regression tasks similarly favored MTL (R2 = 0.94), with multimodal configurations outperforming single-modality baselines. Further comparisons with classical machine learning models and previously developed cardiotoxicity models demonstrated the superiority of Cardiosim-Tox across all endpoints. SHAP analysis confirmed that feature-activity relationships, both at global and mechanistic levels, align with known ion-channel pharmacophores, enhancing interpretability beyond predictive accuracy. Overall, Cardiosim-Tox provides a reliable and interpretable platform for integrated multi-channel cardiac safety assessment, supporting early-stage cardiotoxicity screening in drug development in line with the CiPA paradigm. The Cardiosim-Tox web server is accessible at https://metaheart.kr/login .
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Cardiosim-Tox: an interpretable multitask deep learning QSAR platform with multimodal feature fusion for predicting hERG, Cav1.2, and Nav1.5 blockade risk and potency. — 科研速览 Science Skim