Molecular Docking and Dynamics in Computational Cancer Drug Discovery

Cancer remains one of the most pressing challenges in biomedical research, owing to its molecular complexity, heterogeneity, and frequent resistance to conventional therapies. Traditional drug discovery pipelines are often costly and time-intensive, making it essential to adopt innovative computational strategies that accelerate the identification and optimization of promising therapeutic candidates. Among these strategies, molecular docking and molecular dynamics (MD) simulations have become powerful and complementary tools in structure-based drug design for oncology research.

The primary objective of this Research Topic is to explore how docking and dynamics can be effectively employed to identify, evaluate, and optimize small molecules that target cancer-associated proteins. Docking provides rapid predictions of ligand–protein binding modes and affinities, while molecular dynamics captures the conformational flexibility and atomistic behaviour of biomolecular complexes in biologically relevant environments. Together, these techniques enhance our ability to discover and characterize molecules with improved specificity, stability, and therapeutic potential, while offering mechanistic insights into resistance pathways and off-target effects.

This Research Topic welcomes contributions that integrate computational methods with experimental validation, bridging in silico predictions and translational oncology. The scope includes, but is not limited to:
- Structure-Based Virtual Screening: Utilizing molecular docking to screen large compound libraries against cancer-relevant protein targets to identify high-potential lead molecules.
- Binding Affinity and Mechanistic Insights: Applying docking and dynamics to predict binding free energies, conformational changes, and key molecular interactions critical for therapeutic efficacy.
- Lead Optimization: Employing iterative docking and MD simulations to refine hit compounds, improve pharmacological properties, and minimize toxicity.
- Resistance Mechanisms: Investigating how mutations or post-translational modifications in cancer-related proteins influence drug binding and stability, thereby guiding next-generation inhibitor design.
This interdisciplinary field integrates computational biology, structural bioinformatics, and cheminformatics to accelerate cancer drug discovery. By combining molecular modelling with experimental insights, this Research Topic aims to foster collaborative efforts that drive the development of novel, effective, and personalized cancer therapeutics.

Please note: manuscripts based solely on computational predictions or analyses of public datasets, without validation in independent biological or clinical contexts, will not be considered.