KLSD: A Curated Kinase–Ligand Database Mapping Selectivity Landscapes and Polypharmacology

Herein, we present a database, KLSD, which is a curated resource of 787 213 small-molecule kinase inhibitors annotated with 1.8 M quantitative activity records across 428 human kinases, emphasizing selectivity and polypharmacology. Moreover, we introduce a dual-task ensemble that simultaneously regresses pAct and computes selectivity scores. The core is a multibranch residual multilayer perceptron (MLP) whose branches are kinase-specific; this is augmented by SVM, RF, XGBoost, CNN, GCN, GAT, RGCN and VAE-enhanced graph nets. Continuous potency labels replace categorical classes to improve resolution. Benchmarked on the JAK family (JAK1/2/3, TYK2), the ensemble achieves classification accuracies of ≥0.84 for each kinase and 0.98 overall, demonstrating strong generalizability. KLSD and models are freely available at http://ai.njucm.edu.cn:8080.

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Publication Details

Journal
ACS Omega
Published
2026-02-23
DOI
https://doi.org/10.1021/acsomega.5c11061
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
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article

KLSD: A Curated Kinase–Ligand Database Mapping Selectivity Landscapes and Polypharmacology

Chang Li, Shanliang Sun, Xufeng Lang, YiHua SONG et al.
ACS Omega
Computational Drug Discovery Methods
article

KLSD: A Curated Kinase–Ligand Database Mapping Selectivity Landscapes and Polypharmacology

Chang Li, Shanliang Sun, Xufeng Lang, YiHua SONG, Hongyan Li, Yuqian Yuan, Zuojian Zhou, Xuebin Qiao, Muzi Liang, Ye Yang
article en

Abstract

Herein, we present a database, KLSD, which is a curated resource of 787 213 small-molecule kinase inhibitors annotated with 1.8 M quantitative activity records across 428 human kinases, emphasizing selectivity and polypharmacology. Moreover, we introduce a dual-task ensemble that simultaneously regresses pAct and computes selectivity scores. The core is a multibranch residual multilayer perceptron (MLP) whose branches are kinase-specific; this is augmented by SVM, RF, XGBoost, CNN, GCN, GAT, RGCN and VAE-enhanced graph nets. Continuous potency labels replace categorical classes to improve resolution. Benchmarked on the JAK family (JAK1/2/3, TYK2), the ensemble achieves classification accuracies of ≥0.84 for each kinase and 0.98 overall, demonstrating strong generalizability. KLSD and models are freely available at http://ai.njucm.edu.cn:8080.

ACS OmegaVol. 11(9)
Nanjing University of Chinese Medicine (CN), Nanjing Normal University (CN), Henan University of Traditional Chinese Medicine (CN), Holistic Management International (US), Centre de Recherche en Informatique (FR), Epidemic Intelligence Service (US), Integrative Medicine Institute (US), Hong Kong Metropolitan University (HK)
Openalex Percentile: Top 44%
Computational Drug Discovery Methods
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