Current medicinal chemistry · 2026
Cheminformatics and Machine Learning-Driven QSAR Analysis of SPHK2 Inhibitors for Anticancer Drug Design.
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Hussein Muataz Naeem, Hussein Shahad Nahedh, Oraibi Amjad Ibrahim, Alburghaif Ali H, Al-Hussainy Hany Aqeel, Almukram Ali Majeed Ali, Shakeel Faiyaz
Аннотация
INTRODUCTION: Sphingosine 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.
METHODS: A 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, R2, and Q2 metrics. Top-ranked compounds were subsequently evaluated using Glide docking and MMGBSA energy calculations.
RESULTS: Random Forest models demonstrated the best predictive performance, especially with Klekota-Roth and PubChem descriptors (R2 = 0.78; Q2 = 0.72). Lead compounds, namely CHEMBL2409888 and CHEMBL2409889, exhibited strong binding affinities (ΔG = -58.02 and -58.13 kcal/mol), interacting with key residues GLU293, ARG652, and HIS289. Inactive compounds showed reduced binding strength and limited residue interactions.
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.
CONCLUSION: 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.
METHODS: A 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, R2, and Q2 metrics. Top-ranked compounds were subsequently evaluated using Glide docking and MMGBSA energy calculations.
RESULTS: Random Forest models demonstrated the best predictive performance, especially with Klekota-Roth and PubChem descriptors (R2 = 0.78; Q2 = 0.72). Lead compounds, namely CHEMBL2409888 and CHEMBL2409889, exhibited strong binding affinities (ΔG = -58.02 and -58.13 kcal/mol), interacting with key residues GLU293, ARG652, and HIS289. Inactive compounds showed reduced binding strength and limited residue interactions.
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.
CONCLUSION: 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.