Progress in molecular docking

TLDR

Molecular docking has become a key tool in computer‑assisted drug design, improving efficiency and reducing research cost by predicting binding affinity and analyzing interaction modes. This study introduces the key principles, procedures, and widely‑used applications of molecular docking, compares common docking programs, recommends suitable research areas, and proposes integrating large biological data into scoring functions to enhance accuracy. The authors compare commonly used docking applications and recommend which research areas are best suited for each. The review highlights recent advances such as integrated methods and deep learning, but notes that incomplete molecular structures and scoring function limitations still hinder accurate binding affinity predictions.

Abstract

Background In recent years, since the molecular docking technique can greatly improve the efficiency and reduce the research cost, it has become a key tool in computer‐assisted drug design to predict the binding affinity and analyze the interactive mode. Results This study introduces the key principles, procedures and the widely‐used applications for molecular docking. Also, it compares the commonly used docking applications and recommends which research areas are suitable for them. Lastly, it briefly reviews the latest progress in molecular docking such as the integrated method and deep learning. Conclusion Limited to the incomplete molecular structure and the shortcomings of the scoring function, current docking applications are not accurate enough to predict the binding affinity. However, we could improve the current molecular docking technique by integrating the big biological data into scoring function.