Free energy perturbation and machine learning-assisted identification of potential MAP3K8 hit molecules: a comprehensive structure- and ligand-based studies.

A multistage computational pipeline integrating ligand-based and structure-based screening with ADMET evaluation, molecular dynamic simulation and free energy calculations for the discovery of potential TNIK inhibitors.

This LBVS-SBVS-ADMET-MD pipeline effectively identified three promising TNIK inhibitors, providing a solid foundation for future experimental validation and potential development of targeted therapies for Wnt-driven malignancies.

Harnessing machine learning, docking and molecular dynamics for the virtual screening of compounds as CDK4/6 dual inhibitors.

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.

Structure-based discovery of diphenylpyrazines as disruptors of the Skp2–Cks1 protein–protein interaction: computational insights and biochemical validation

Findings validate the diphenylpyrazine scaffold as a promising chemotype for Skp2–Cks1 inhibition and identify C3 as a strong lead for further optimization.

Computational structure-based screening identifies potential inhibitors of TRIM33α

The identified compounds may serve as valuable starting points for further experimental validation and structural optimization toward the development of novel anticancer agents targeting TRIM33, and establishes a robust computational framework for developing TRIM33α-targeted therapies.

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.