Evidence map›Paper›PMID 41664334›Full record

ArticleCurrent medicinal chemistry2026

Cheminformatics and Machine Learning-Driven QSAR Analysis of SPHK2 Inhibitors for Anticancer Drug Design.

Muataz Naeem Hussein, Shahad Nahedh Hussein, Amjad Ibrahim Oraibi, Ali H Alburghaif, Hany Aqeel Al-Hussainy, Ali Majeed Ali Almukram, Faiyaz Shakeel

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

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Muataz Naeem HusseinDepartment of Pharmacology, College of Medicine, AL-Nahrain University, Baghdad, Iraq.
Shahad Nahedh HusseinBiotechnology Department, College of Science, University of Baghdad, Baghdad, Iraq.
Amjad Ibrahim OraibiAl-Manara College for Medical Sciences, Department of Pharmacy, MISAN, 62001, Iraq.
Ali H AlburghaifDepartment of Pharmacology, Ibn Sina University for Medical and Pharmaceutical Sciences, Baghdad, Iraq.
Hany Aqeel Al-HussainyDepartment of Pharmacology, College of Pharmacy, Al-Nisour University College, Baghdad, Iraq.
Ali Majeed Ali AlmukramPharmacy School, University of Maryland, Baltimore, MD, USA.
Faiyaz ShakeelDepartment of Pharmaceutics, College of Pharmacy, King Saud University, Riyadh, 11451, Saudi Arabia.

Funding

King Saud University, Riyadh, Saudi Arabia ORF-2026-1040
6 · The paper itself

Abstract

introductionSphingosine kinase 2 (SPHK2) plays a pivotal role in sphingolipid metabolism and has emerged as a therapeutic target in cancer due to its involvement in tumor proliferation and resistance mechanisms.

methodsA dataset of 269 SPHK2-targeting compounds from ChEMBL was analyzed using five molecular descriptor sets: PubChem, MACCS, CDK, Substructure, and Klekota- Roth. Six machine learning algorithms were applied to develop QSAR models, and they were validated using ROC-AUC, PCA, R

resultsRandom Forest models demonstrated the best predictive performance, especially with Klekota-Roth and PubChem descriptors (R DISCUSSION: This integrative computational pipeline successfully identified structurally significant SPHK2 inhibitors and highlighted molecular features contributing to activity. While the results offer mechanistic insights and a rational framework for further optimization, findings are based solely on in silico predictions.

conclusionThis study presents a predictive framework combining machine learning and molecular modeling to identify selective SPHK2 inhibitors, offering valuable candidates for further synthesis and biological validation.

Indexed as

Antineoplastic AgentsDrug DesignEnzyme InhibitorsMachine LearningPhosphotransferases (Alcohol Group Acceptor)Quantitative Structure-Activity RelationshipHumansMolecular Docking SimulationSphingosine KinaseAntineoplastic AgentsEnzyme InhibitorsPhosphotransferases (Alcohol Group Acceptor)Sphingosine Kinasesphingosine kinase 2, humancancermachine learningMMGBSAmolecular modellingQSARRandom ForestSphingosine kinase 2

Identifiers

PMID41664334

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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 Socratic graph.