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

This 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.

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Publication Details

Journal
Current Medicinal Chemistry
Published
2026-02-03
DOI
https://doi.org/10.2174/0109298673390041251026153630
Primary Topic
Computational Drug Discovery Methods
Type
article
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article

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

Muataz Naeem Hussein, Faiyaz Shakeel, Amjad Ibrahim Oraibi, Ali Majeed Ali Almukram et al.
Current Medicinal Chemistry
Computational Drug Discovery Methods
article

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

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

Abstract

This 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.

Current Medicinal ChemistryVol. 33
University of Maryland, Baltimore (US), University of Baghdad (IQ), King Saud University (SA), University of Misan (IQ), Al-Nisour University College (IQ), Nahrain University (IQ)
Openalex Percentile: Top 59%
Computational Drug Discovery Methods
DOISourceOpenAlex
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