Database Commons

a catalog of worldwide biological databases

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.

Due to the similarity and diversity among kinases, small molecule kinase inhibitors (SMKIs) often display multi-target effects or selectivity, which have a strong correlation with the efficacy and safety of these inhibitors. However, due to the limited number of well-known popular databases and their restricted data mining capabilities, along with the significant scarcity of databases focusing on the pharmacological similarity and diversity of SMIKIs, researchers find it challenging to quickly access relevant information. The KLIFS database is representative of specialized application databases in the field, focusing on kinase structure and co-crystallised kinase-ligand interactions, whereas the KLSD database in this paper emphasizes the analysis of SMKIs among all reported kinase targets. To solve the current problem of the lack of professional application databases in kinase research and to provide centralized, standardized, reliable and efficient data resources for kinase researchers, this paper proposes a research program based on the ChEMBL database. It focuses on kinase ligands activities comparisons. This scheme extracts kinase data and standardizes and normalizes them, then performs kinase target difference analysis to achieve kinase activity threshold judgement. It then constructs a specialized and personalized kinase database platform, adopts the front-end and back-end separation technology of SpringBoot architecture, constructs an extensible WEB application, handles the storage, retrieval and analysis of the data, ultimately realizing data visualization and interaction. This study aims to develop a kinase database platform to collect, organize, and provide standardized data related to kinases. By offering essential resources and tools, it supports kinase research and drug development, thereby advancing scientific research and innovation in kinase-related fields. It is freely accessible at: http://ai.njucm.edu.cn:8080.