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<p>Breast cancer is one of the most prevalent and lethal malignancies affecting women globally. The increasing resistance to current therapeutic strategies highlights the need for novel molecular targets. Inositol-requiring enzyme 1 alpha (IRE1α), a key sensor in the unfolded protein response (UPR), has emerged as a promising therapeutic target due to its role in tumour progression and survival. This study employed an integrative in silico approach combining machine learning, molecular docking, and molecular dynamics simulations to identify potent, non-toxic IRE1α inhibitors for breast cancer treatment. An initial library of 115 compounds retrieved from ChEMBL and MedChemExpress was used for machine learning-based toxicity modelling. Literature curation identified 44 reported IRE1α inhibitors, which were reduced to 38 unique compounds following duplicate removal. Drug-likeness and ADMET screening using SwissADME and ProTox retained 22 compounds for further evaluation. Molecular docking was performed using AutoDock, followed by Dynamics simulations in GROMACS to assess stability. Machine Learning (ML) models were developed for both toxicity regression and binary toxicity classification analyses. Toxicity prediction models were developed using twenty physicochemical and pharmacokinetic descriptors. In the regression analysis, Random Forest demonstrated the strongest cross-validation performance (R² = 0.5998 ± 0.3439), while the stacking ensemble achieved the highest test-set performance (R² = 0.9765), although differences among ensemble methods were not statistically significant. In the complementary classification analysis, the Support Vector Machine (SVM) achieved the highest discriminative performance with an ROC-AUC value of 0.98. Docking studies revealed that Z4P exhibited the strongest binding affinity (− 7.93&#xa0;kcal/mol) to the wild-type IRE1, compared with the control drug MKC8866 (− 6.7&#xa0;kcal/mol). Additionally, Z4P exhibited a higher binding energy of − 9.5&#xa0;kcal/mol, whereas MKC8866 had a binding energy of − 6.94&#xa0;kcal/mol. MD simulations over 200 ns confirmed the stability of the IRE1–Z4P complex, with favourable RMSD, RMSF, Rg, and SASA profiles relative to the control. These findings highlight Z4P as a promising mutation-resilient IRE1 inhibitor and validate the effectiveness of the integrated computational pipeline for identifying potential anti-cancer therapeutics.</p>

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Integrating machine learning and structure-based simulations to prioritise Z4P as a mutation-resilient IRE1α inhibitor for breast cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月2日

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  • Nithisha L. Bastin,
  • P. K. Praveen Kumar,
  • Baranitharan Ethiraj,
  • Ravikumar Rajarathinam,
  • J. Shrihari,
  • Rajkumar Sivanraju

摘要

Breast cancer is one of the most prevalent and lethal malignancies affecting women globally. The increasing resistance to current therapeutic strategies highlights the need for novel molecular targets. Inositol-requiring enzyme 1 alpha (IRE1α), a key sensor in the unfolded protein response (UPR), has emerged as a promising therapeutic target due to its role in tumour progression and survival. This study employed an integrative in silico approach combining machine learning, molecular docking, and molecular dynamics simulations to identify potent, non-toxic IRE1α inhibitors for breast cancer treatment. An initial library of 115 compounds retrieved from ChEMBL and MedChemExpress was used for machine learning-based toxicity modelling. Literature curation identified 44 reported IRE1α inhibitors, which were reduced to 38 unique compounds following duplicate removal. Drug-likeness and ADMET screening using SwissADME and ProTox retained 22 compounds for further evaluation. Molecular docking was performed using AutoDock, followed by Dynamics simulations in GROMACS to assess stability. Machine Learning (ML) models were developed for both toxicity regression and binary toxicity classification analyses. Toxicity prediction models were developed using twenty physicochemical and pharmacokinetic descriptors. In the regression analysis, Random Forest demonstrated the strongest cross-validation performance (R² = 0.5998 ± 0.3439), while the stacking ensemble achieved the highest test-set performance (R² = 0.9765), although differences among ensemble methods were not statistically significant. In the complementary classification analysis, the Support Vector Machine (SVM) achieved the highest discriminative performance with an ROC-AUC value of 0.98. Docking studies revealed that Z4P exhibited the strongest binding affinity (− 7.93 kcal/mol) to the wild-type IRE1, compared with the control drug MKC8866 (− 6.7 kcal/mol). Additionally, Z4P exhibited a higher binding energy of − 9.5 kcal/mol, whereas MKC8866 had a binding energy of − 6.94 kcal/mol. MD simulations over 200 ns confirmed the stability of the IRE1–Z4P complex, with favourable RMSD, RMSF, Rg, and SASA profiles relative to the control. These findings highlight Z4P as a promising mutation-resilient IRE1 inhibitor and validate the effectiveness of the integrated computational pipeline for identifying potential anti-cancer therapeutics.