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Efficient Protein-Ligand Binding Free Energy Estimation with Coarse-Grained Funnel Metadynamics.

Andrea Grazzi¹, Chelsea M Brown², Maurizio Sironi¹

¹Department of Chemistry, University of Milan, Via C. Golgi 19, 20133 Milan, Italy.

Journal of Chemical Theory and Computation

|January 10, 2026

Summary

Coarse-grained funnel metadynamics (CG-FMD) offers accurate protein-ligand binding free energy predictions comparable to all-atom molecular dynamics (AA-MD). This method significantly reduces computational cost, enabling high-throughput screening for drug discovery.

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Area of Science:

Computational chemistry
Biophysics
Drug discovery

Background:

Accurate prediction of protein-ligand binding free energy remains a challenge.
All-atom molecular dynamics (AA-MD) is accurate but computationally expensive.
Docking methods are fast but lack accuracy.

Purpose of the Study:

To develop a computationally efficient method for accurate binding free energy prediction.
To bridge the gap between AA-MD accuracy and docking throughput.
To validate coarse-grained funnel metadynamics (CG-FMD) for binding free energy calculations.

Main Methods:

Coarse-grained funnel metadynamics (CG-FMD) using the Martini 3 force field.
Modeling colchicine binding to two protein targets at both all-atom (AA) and coarse-grained (CG) resolutions.
Extensive simulations totaling over 7 ms to assess prediction robustness.

Main Results:

CG-FMD predictions for ΔGbind were comparable to experimental values.
The method achieved this accuracy with a fraction of the computational cost of AA-MD.
Extensive sampling reduced statistical uncertainty, compensating for the simplified CG representation.

Conclusions:

CG-FMD provides a robust and computationally efficient approach for predicting protein-ligand binding free energies.
This method holds promise for accelerating drug discovery by enabling high-throughput screening.
Further studies should expand the range of ligands and targets investigated.