DiffDock vs GNINA vs AutoDock Vina: AI-Age Docking, Honestly Compared

By BioDockify Computational Research Team · October 11, 2026 · Comparison

Three docking philosophies compared: Vina\\'s empirical physics, GNINA\\'s deep-learning scoring, and DiffDock\\'s diffusion-based pose generation - benchmark numbers, speed, flexibility handling, blind-docking ability, and which to use for which job.

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

Key References

• Trott, O. & Olson, A.J. AutoDock Vina. J. Comput. Chem. 31, 445-461 (2010). DOI: 10.1002/jcc.21334
• McNutt, A.T., Francoeur, P., Aggarwal, R. et al. GNINA 1.0: molecular docking with deep learning. J. Cheminform. 13, 43 (2021). DOI: 10.1186/s13321-021-00521-9
• Corso, G., St"ark, H., Jing, B., Barzilay, R. & Jaakkola, T. DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking. ICLR (2023). arXiv:2210.01776
• Francoeur, P.G., Masuda, T., Sunseri, J. et al. Three-Dimensional Convolutional Neural Networks and a Cross-Docked Data Set for Structure-Based Drug Design. J. Chem. Inf. Model. 60, 4200-4215 (2020). DOI: 10.1021/acs.jcim.0c00411
• Eberhardt, J., Santos-Martins, D., Tillack, A.F. & Forli, S. AutoDock Vina 1.2.0. J. Chem. Inf. Model. 61, 3891-3898 (2021). DOI: 10.1021/acs.jcim.1c00203

Scope & Limitations

Author-reported benchmark numbers are not directly comparable across papers (different test sets, different RMSD thresholds); treat cross-paper percentages as indicative, not definitive Diff-class methods evolve monthly; check each tool's current release before relying on any specific capability claim