Aug 2026· Current Issues in Molecular Biology· Vol 48, pp. 855· 0 citations· 38 references
TL;DR
Results demonstrate that approaches incorporating machine learning and structure-based computational analysis can be useful for discovering and prioritizing potential IRAK4 inhibitor candidates.
Abstract
Interleukin-1 receptor-associated kinase 4 (IRAK4) is one of the IRAK family proteins and plays an important role in the regulation of innate and inflammatory responses. In particular, IRAK4 acts as a key regulator of the Toll-like receptor (TLR) and interleukin-1 receptor (IL-1R) signaling pathways and has attracted attention as a therapeutic target for immune and inflammatory diseases. In this study, an integrated computational approach combining machine learning, molecular docking, and molecular dynamics simulations was applied to identify putative IRAK4 inhibitor candidates. Bioactivity data of IRAK4 were obtained from the ChEMBL and PubChem databases and evaluated for multiple binary classification models. The optimized XGBoost model based on ECFP4 and PubChem fingerprints achieved an ROC-AUC of 0.996 and an average precision (AP) of 0.991 on the independent test set. After that, 20 candidate compounds with high predictive probability score were finally selected through subsequent screening of the DrugBank database. Among them, DB12168 (MK-0557), DB15040 (TP-271), and DB18152 (Zilurgisertib) exhibited favorable binding free energies and stable complex formation with IRAK4 through molecular dynamics simulations and MM-PBSA calculations. Overall, these results demonstrate that approaches incorporating machine learning and structure-based computational analysis can be useful for discovering and prioritizing potential IRAK4 inhibitor candidates.
The proto-oncogene serine/threonine kinase PIM2 is a critical regulator of cell proliferation, survival, and tumor progression and represents an attractive therapeutic target for several cancers. In this study, an integrated machine learning–guided computational pipeline was developed to identify potential PIM2 inhibitors by combining quantitative structure–activity relationship (QSAR) modeling, virtual screening, molecular docking, molecular dynamics (MD) simulations, and pharmacokinetic prediction. Bioactivity data for PIM2 inhibitors were retrieved from the ChEMBL database, yielding 5953 compounds. After data cleaning, structural standardization, and removal of duplicates and invalid entries, a curated dataset of 1584 compounds was obtained for QSAR modeling. To address dataset imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied before model development. Twelve molecular fingerprint descriptors were generated and used to construct 180 QSAR models using five machine learning algorithms, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), k-Nearest Neighbors (KNN), and Multilayer Perceptron (MLP). Among these models, the Random Forest–fingerprint model demonstrated the best predictive performance, achieving a mean R2 of 0.971 with low prediction errors (RMSE = 0.271; MAE = 0.125) across training, testing, and cross-validation datasets. The optimized model was subsequently applied to virtual screening of multiple chemical libraries, including FDA-approved drugs, natural product databases, and commercial compound collections. Several promising candidates were identified, including TCMBANKIN000009 (emetine), Amb28533044 (4,6′-Anhydrooxysporidinone), NPC170963 (Lysophosphatidylcholine (15:0)), NPC262768 (Endosulfan), and NPC469603 (8-hydroxyircinialactam A). Molecular docking showed that these compounds bind within the ATP-binding pocket of PIM2 kinase, forming interactions with key residues such as Lys62, Asp125, Asp128, and Glu168. Subsequent molecular dynamics simulations confirmed the stability of selected complexes, demonstrating reduced residue fluctuations, stable protein compactness, and persistent intermolecular interactions during the simulation. Furthermore, ADMET prediction suggested favorable pharmacokinetic and toxicity profiles for several compounds. Collectively, these findings highlight the potential of the identified molecules as promising PIM2 inhibitor candidates, providing valuable leads for future experimental validation and anticancer drug development.
A. Fahira, M. Shahab, Zaheer Ud Din et al.· Journal of Genetic Engineeri...· 0 citations
This work demonstrates that integrating docking-derived pharmacophores with conformational ensemble-based machine learning provides an effective approach for discovering novel inhibitors against underexplored kinase targets.
G. Shakhatreh, M. Taha, S. Daoud· RSC Advances· 0 citations
The convergence of docking, dynamics, and free-energy results prioritized PM2, PM3, and PM4 as promising MAP3K8 hit candidates, which require further experimental validation and lead optimization.
M. Islam, A. Iqbal, M. A. Ali et al.· Molecular diversity· 0 citations
The proposed workflow efficiently reduced a large chemical space to a focused set of TNKS1 inhibitor candidates while substantially reducing the experimental screening burden, highlighting the value of integrating consensus ML, SBVS, and experimental validation to accelerate early-stage hit discovery for TNKS1 and other therapeutic targets.
M. Bilotta, Adriana Gargano, R. Rocca et al.· Pharmaceuticals· 0 citations
FYN kinase is a non-receptor protein tyrosine kinase involved in various cancers and neurodegenerative diseases; however, no selective FYN inhibitor has been approved yet. Here we introduce the explainable Machine Learning (ML) coupled with virtual screening and Molecular Docking (MD) pipeline for fast prediction of new FYN kinase inhibitors. In this study, we constructed the training set of 906 molecules active against FYN kinase from the ChEMBL database. Molecules were encoded with Extended-Connectivity Fingerprints (ECFP4). The classification models Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) were developed, and the latter showed the better performance in test (AUC=0.8118) and 5-fold cross-validation (AUC=0.8297). Based on the SHapley Additive exPlanations (SHAP) values obtained via TreeExplainer, nitrogen-containing heterocycles and hydrogen bond acceptors have been identified as the most important molecular substructures. Using the optimal XGBoost classifier, screening of 2,000 approved drugs has been performed, resulting in 470 hit molecules (23.5% hit rate). Five best molecules were further submitted to the MD procedure using AutoDock Vina to dock to FYN kinase domain (PDB RCSB: 2DQ7), showing binding energies in the interval of -9.57 to -6.32 kcal/mol. Dasatinib Anhydrous (CHEMBL1421) was the second strongest binder (-8.49 kcal/mol), effectively interacting with the ATP binding site. Although CHEMBL1171837 was the strongest binder (-9.57 kcal/mol), it was caught in the ADMET profiling. According to ADMET profiling, the top one inhibitor (CHEMBL1421) satisfies Lipinski’s rule of five and Veber rules. Analysis of hydrogen bond and hydrophobic interactions revealed hydrogen bonding with ASP148, LYS39, and ASN86 and hydrophobic interactions with ALA147, ILE80, and GLY88. Validation by self-docking procedure (self-docking or STS) showed low Root Mean Square Deviation (RMSD)<2.0 Å with a binding affinity of -11.53 kcal/mol. This work highlights how explainable ML can be used in combination with structure-based docking to expedite the drug discovery process against FYN kinase and can be applied to other kinase targets.
Ahmet Turan Demir· Intelligent Systems Research...· 4 citations
An integrated computational framework combining machine learning (ML), deep learning (DL), and structure-based docking with experimental validation identifies AO65 as a promising lead for further TDP1-focused investigation.
Huang Zeng, Manyi Zhang, Bo Qiu et al.· RSC Advances· 0 citations
