Molecular Docking: Principles, Workflow, Applications, and Limitations

By BioDockify Computational Research Team · August 8, 2026 · Molecular Docking

A comprehensive research guide to molecular docking — the computational method that predicts protein-ligand binding. Covers scoring functions, search algorithms, applications in drug discovery, and critical limitations every researcher should understand.

Read the complete article with tables, code and references on BioDockify.

Key References

• Trott, O. & Olson, A.J. AutoDock Vina: improving the speed and accuracy of docking. J. Comput. Chem. 31, 455-461 (2010). DOI: 10.1002/jcc.21334
• McNutt, A.T. et al. GNINA: molecular docking with deep learning. J. Cheminform. 13, 43 (2021). DOI: 10.1186/s13321-021-00522-2
• Li, H. et al. RF-Score v3: improved model for predicting protein-ligand affinity. J. Cheminform. 12, 20 (2020). DOI: 10.1186/s13321-020-00425-4
• Morris, G.M. et al. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J. Comput. Chem. 30, 2785-2791 (2009). DOI: 10.1002/jcc.21256
• Berman, H.M. et al. The Protein Data Bank. Nucleic Acids Res. 28, 235-242 (2000). DOI: 10.1093/nar/28.1.235

Scope & Limitations

Scoring functions are approximations — docking scores are NOT binding affinities Protein flexibility is limited (rigid receptor by default in most engines) Solvent effects and entropy are not fully captured by empirical scoring functions