25 Years of Cardiac Ion Channel QSAR: From hERG Dominance to Multi-Channel Modeling.
- 1Institute for Complex Molecular Systems (ICMS), Department of Biomedical Engineering, Eindhoven University of Technology, P.O. Box 513, Eindhoven5600 MB, The Netherlands.
- 2Machine Learning Research, Research and Development , Bayer AG, Müllerstr. 178, Berlin13353, Germany.
- 3Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, Milan20156, Italy.
Abstract
In 2013 the Comprehensive In Vitro Proarrhythmia Assay (CiPA) initiative proposed multi-channel assessment for proarrhythmic risk evaluation, yet ligand-based predictive modeling has remained largely single-channel. To quantify and explain this mismatch, we survey 155 ligand-based modeling studies on cardiac ion channels (including conventional QSAR, machine learning, and pharmacophore- and other 3D ligand-based approaches) published between 2001 and mid-2026. Across this period, 88.4% of studies target hERG as their sole ion channel endpoint, a proportion that reflects data availability, regulatory incentives, and research inertia rather than scientific necessity. Although public data for Nav1.5 and Cav1.2 were sufficient for modeling years earlier, both channels received only isolated studies until 2022. Since then, multi-channel modeling has expanded substantially, with multiple groups now addressing the primary channels (hERG, Nav1.5, and Cav1.2) jointly. In contrast, the secondary channels (Kv7.1, Kv4.3, and Kir2.1) remain largely understudied: Kv4.3 and Kir2.1 are entirely unmodeled in the surveyed literature, and models for Kv7.1 remain scarce, likely due to the sparsity and heterogeneity of public data. A retrospective analysis of the reported model performance found no discernible improvements attributable to methodological advances. Reliability-related aspects, such as applicability domain and uncertainty quantification, were also rarely addressed. The field should treat joint modeling of the primary channels as the expected standard rather than a novel contribution. Other important aspects include the development of standardized benchmarks for multi-channel evaluation, integration with physiological models, and greater emphasis on prediction reliability. For the secondary channels, progress would additionally require coordinated data release or federated learning, methods suited to data-scarce targets, and community coordination, none of which individual research groups can establish alone.
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BibTeX
@article{iwan2026years,
title = {{25 Years of Cardiac Ion Channel QSAR: From hERG Dominance to Multi-Channel Modeling.}},
author = {Mateusz Iwan and Francesca Grisoni and Anastasia Pentina and Marina Garcia de Lomana and Alessandra Roncaglioni},
journal = {Journal of chemical information and modeling},
year = {2026},
month = sep,
doi = {10.1021/acs.jcim.6c03072},
url = {https://europepmc.org/article/MED/42906067},
note = {CC BY},
}
RIS
TY - JOUR
TI - 25 Years of Cardiac Ion Channel QSAR: From hERG Dominance to Multi-Channel Modeling.
AU - Mateusz Iwan
AU - Francesca Grisoni
AU - Anastasia Pentina
AU - Marina Garcia de Lomana
AU - Alessandra Roncaglioni
PY - 2026
DA - 2026/09/01
JO - Journal of chemical information and modeling
DO - 10.1021/acs.jcim.6c03072
UR - https://europepmc.org/article/MED/42906067
AB - In 2013 the Comprehensive In Vitro Proarrhythmia Assay (CiPA) initiative proposed multi-channel assessment for proarrhythmic risk evaluation, yet ligand-based predictive modeling has remained largely single-channel. To quantify and explain this mismatch, we survey 155 ligand-based modeling studies on cardiac ion channels (including conventional QSAR, machine learning, and pharmacophore- and other 3D ligand-based approaches) published between 2001 and mid-2026. Across this period, 88.4% of studies target hERG as their sole ion channel endpoint, a proportion that reflects data availability, regulatory incentives, and research inertia rather than scientific necessity. Although public data for Nav1.5 and Cav1.2 were sufficient for modeling years earlier, both channels received only isolated studies until 2022. Since then, multi-channel modeling has expanded substantially, with multiple groups now addressing the primary channels (hERG, Nav1.5, and Cav1.2) jointly. In contrast, the secondary channels (Kv7.1, Kv4.3, and Kir2.1) remain largely understudied: Kv4.3 and Kir2.1 are entirely unmodeled in the surveyed literature, and models for Kv7.1 remain scarce, likely due to the sparsity and heterogeneity of public data. A retrospective analysis of the reported model performance found no discernible improvements attributable to methodological advances. Reliability-related aspects, such as applicability domain and uncertainty quantification, were also rarely addressed. The field should treat joint modeling of the primary channels as the expected standard rather than a novel contribution. Other important aspects include the development of standardized benchmarks for multi-channel evaluation, integration with physiological models, and greater emphasis on prediction reliability. For the secondary channels, progress would additionally require coordinated data release or federated learning, methods suited to data-scarce targets, and community coordination, none of which individual research groups can establish alone.
SN - 1549-9596
SN - 1549-960X
ER -