Synergistic approach utilizing bioinformatics, machine learning, and traditional screening for the identification of novel CSK inhibitors targeting hepatocellular carcinoma.

Yang Lu, Bizhi Li, Xiaoli Zheng, Lei Xu, Linghui Zeng, Chong Zhang, Jiankang Zhang

Abstract read

In one paragraph

Article in Journal of computer-aided molecular design, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in

1citing 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

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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

1 citing paper in PubMed.

Multimodal machine learning and deep graph neural networks for the prediction of molecular inhibitory activity and disease associations.Journal of computer-aided molecular design · 2026
Article

4 · The record

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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

7 authors.

Yang Lu *Key Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, Hangzhou City University, Hangzhou, 310015, China. luyang@hzcu.edu.cn.

Bizhi Li *Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.

Xiaoli ZhengKey Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, Hangzhou City University, Hangzhou, 310015, China.

Lei XuInstitute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, 213001, China.

Linghui ZengKey Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, Hangzhou City University, Hangzhou, 310015, China.

Chong ZhangKey Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, Hangzhou City University, Hangzhou, 310015, China.

Jiankang ZhangKey Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, Hangzhou City University, Hangzhou, 310015, China. zhang_jk@hzcu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The overexpression or activation of C-terminal Src kinase (CSK) has been recognized as a pivotal factor in the progression of hepatocellular carcinoma (HCC), positioning CSK as a promising therapeutic target. Despite this potential, no CSK-specific inhibitors have been developed for HCC treatment to date. Addressing this gap, our study established a robust virtual screening protocol that integrates energy-based screening techniques with machine learning methodologies. Through this systematic approach, we identified a novel compound, 6, exhibiting potent CSK inhibitory activity, as evidenced by an IC

Indexed as

Antineoplastic AgentsCarcinoma, HepatocellularComputational BiologyCSK Tyrosine-Protein KinaseLiver NeoplasmsMachine LearningProtein Kinase Inhibitorssrc-Family KinasesCell Line, TumorCell ProliferationHumansMolecular Docking SimulationMolecular Dynamics SimulationAntineoplastic AgentsCSK protein, humanCSK Tyrosine-Protein KinaseProtein Kinase Inhibitorssrc-Family KinasesBioinformaticsCSKHCCMachine learningMolecular dynamicVirtual screening

Identifiers

PMID41204015

DOI10.1006/jmbi.1998.2369

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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.