Identification of novel PKR inhibitory chemotypes via integrated molecular modelling and machine learning

Machine learning–driven identification of PIM2 kinase inhibitors through QSAR modeling and molecular dynamics simulations

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.

Structure-based virtual screening and lead identification of novel protein kinase inhibitors through integrated computational approaches

Machine Learning-Driven Drug Repositioning Identifies Putative IRAK4 Inhibitors Through Structure-Based Computational Evaluation

Results demonstrate that approaches incorporating machine learning and structure-based computational analysis can be useful for discovering and prioritizing potential IRAK4 inhibitor candidates.

QSAR, molecular dynamics, and biological evaluation of novel myeloperoxidase inhibitors via ligand-based pharmacophore modeling as potential anticancer agents

The findings from molecular docking, 500-ns molecular dynamics simulations, MM-GBSA calculations, and alanine scanning analyses collectively corroborate a stable binding mode of BTB11556 within the MPO active site, support further investigation of BTB11556 as a candidate compound associated with MPO-targeted therapeutic strategies.

An Integrated Consensus Machine Learning and Structure-Based Workflow for the Discovery of Novel Tankyrase 1 Inhibitors

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.

From Descriptor Learning to Binding Stability: An Explainable Machine Learning Pipeline for EGFR Double-Mutant Inhibitor Discovery

An integrated computational workflow combining explainable machine learning, virtual screening, molecular dynamics simulations, and binding free-energy calculations to identify novel inhibitors of this drug-resistant EGFR variant may support the development of new therapeutic strategies for overcoming resistance in EGFR-driven cancers.