Recent Advances in Molecular Docking: Methods, Applications, And Challenges in the Research and Discovery of Phytochemical Compounds

Abstract

Molecular docking has emerged as a cornerstone computational technique in modern drug discovery, particularly for identifying therapeutic potential in phytochemical compounds derived from plants. This research examines recent methodological advances in molecular docking, their applications in phytochemical research, and persistent challenges limiting widespread adoption. We explore how improvements in scoring functions, flexible docking algorithms, and machine learning integration have enhanced prediction accuracy for protein-ligand interactions. The study demonstrates that contemporary docking approaches successfully identify promising phytochemical candidates with significantly improved hit rates compared to traditional screening methods. However, challenges remain regarding water molecule treatment, protein flexibility representation, and accurate binding affinity prediction. Through comprehensive analysis of recent phytochemical docking studies targeting various therapeutic areas including cancer, diabetes, and infectious diseases, we illustrate both the tremendous potential and current limitations of computational screening approaches. Our findings suggest that hybrid methodologies combining molecular docking with molecular dynamics simulations and experimental validation provide the most reliable pathway for phytochemical drug discovery. This research contributes to understanding how computational approaches can accelerate the identification of plant-derived therapeutic compounds while highlighting areas requiring further methodological development to improve prediction reliability and reduce experimental validation costs.