Molecular Dynamics Simulations and Kinetic Measurements to Estimate and Predict Protein-Ligand Residence Times

Luca Mollica, Isabelle Theret, Mathias Antoine, Françoise Perron-Sierra, Yves Charton, Jean Marie Fourquez, Michel Wierzbicki, Jean A. Boutin, Gilles Ferry, Sergio Decherchi, Giovanni Bottegoni, Pierre Ducrot*, Andrea Cavalli

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

48 Citations (Scopus)


Ligand-target residence time is emerging as a key drug discovery parameter because it can reliably predict drug efficacy in vivo. Experimental approaches to binding and unbinding kinetics are nowadays available, but we still lack reliable computational tools for predicting kinetics and residence time. Most attempts have been based on brute-force molecular dynamics (MD) simulations, which are CPU-demanding and not yet particularly accurate. We recently reported a new scaled-MD-based protocol, which showed potential for residence time prediction in drug discovery. Here, we further challenged our procedure's predictive ability by applying our methodology to a series of glucokinase activators that could be useful for treating type 2 diabetes mellitus. We combined scaled MD with experimental kinetics measurements and X-ray crystallography, promptly checking the protocol's reliability by directly comparing computational predictions and experimental measures. The good agreement highlights the potential of our scaled-MD-based approach as an innovative method for computationally estimating and predicting drug residence times.

Original languageEnglish
Pages (from-to)7167-7176
Number of pages10
JournalJournal of Medicinal Chemistry
Issue number15
Early online date8 Jul 2016
Publication statusPublished - 11 Aug 2016

ASJC Scopus subject areas

  • Molecular Medicine
  • Drug Discovery


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