Evidence map›Paper›PMID 42581112›Full record

ArticleArchives of toxicology2026

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

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Article in Archives of toxicology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Fauzan Syarif NursyafiDepartment of Biomedical Engineering, Kumoh National Institute of Technology, Gumi, 39177, Republic of Korea.ORCID http://orcid.org/0009-0001-5330-2008
Byunggyu KangDepartment of Biomedical Engineering, Kumoh National Institute of Technology, Gumi, 39177, Republic of Korea.ORCID http://orcid.org/0009-0000-5397-6451
Junhyeok EomDepartment of Biomedical Engineering, Kumoh National Institute of Technology, Gumi, 39177, Republic of Korea.ORCID http://orcid.org/0009-0002-3517-5372
Rahmafatin Nurul IzzaDepartment of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, 39177, Republic of Korea.ORCID http://orcid.org/0009-0003-0842-027X
Abdul Latif FauzanMeta Heart Co., Ltd, Gumi, 39253, Republic of Korea.ORCID http://orcid.org/0009-0002-0629-6597
Nadilla Hafani PutriDepartment of Biomedical Engineering, Kumoh National Institute of Technology, Gumi, 39177, Republic of Korea.ORCID http://orcid.org/0009-0004-9274-1347
Ulfa Latifa HanumDepartment of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, 39177, Republic of Korea.ORCID http://orcid.org/0009-0001-9129-8951
Yossi OliviaDepartment of Biomedical Engineering, Kumoh National Institute of Technology, Gumi, 39177, Republic of Korea.ORCID http://orcid.org/0009-0006-6934-9616
Sanjida Afrin RoseDepartment of Biomedical Engineering, Kumoh National Institute of Technology, Gumi, 39177, Republic of Korea.ORCID http://orcid.org/0009-0005-5196-0567
Ki Moo LimDepartment of Biomedical Engineering, Kumoh National Institute of Technology, Gumi, 39177, Republic of Korea. kmlim@kumoh.ac.kr.ORCID http://orcid.org/0000-0001-6729-8129

Funding

Korea Innovation Foundation (INNOPOLIS) grant funded by the Korea government (MSIT) RS-2025-04003017Ministry of Science and the Institute of Information & Communications Technology Planning & Evaluation (IITP) through the Innovative Human Resource Development for Local Intellectualization program, funded by the Korea government (MSIT) IITP-2025-RS-2020-II201612National Research Foundation of Korea under the Basic Science Research Program 2022R1A2C2006326Regional Innovation System & Education (RISE)-Regional Growth Innovation LAB program through the Gyeongbuk RISE Center, funded by the Ministry of Education (MOE) and Gyeongsangbuk-do, Republic of Korea 2025-rise-15-105
6 · The paper itself

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 (pIC

Indexed as

Cardiac ion channelsCardiotoxicityComputational toxicologyMulti-task learning (MTL)SHAP analysis

Identifiers

PMID42581112

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.