Rapid Communication - Journal of Pharmaceutical Chemistry & Chemical Science (2023) Volume 7, Issue 2
Combining docking and molecular dynamics for accurate binding free energy estimation
Jeong Min *
Department of Analytical and Molecular Chemistry, Hassan II University of Casablanca, Casablanca, Morocco
- *Corresponding Author:
- Jeong Min
Department of Analytical and Molecular Chemistry,
Hassan II University of Casablanca,
Received: 30-Mar-2023, Manuscript No. AAPCCS-23-94384; Editor assigned: 03-Apr-2023, PreQC No. AAPCCS-23-94384(PQ); Reviewed: 17-Apr-2023, QC No. AAPCCS-23-94384; Revised: 21-Apr-2023, Manuscript No. AAPCCS-23-94384; Published: 28-Apr-2023, DOI:10.35841/aapccs-7.2.139
Citation: Min J. Combining docking and molecular dynamics for accurate binding free energy estimation. J Pharm Chem Chem Sci 2022;7(2):139
Docking is a computational process that predicts the orientation and affinity of a ligand with a protein. Docking and molecular dynamics (MD) simulations are two widely used computational techniques in drug discovery. Docking is a computational method used to predict the binding mode and affinity of a small molecule ligand for its protein target. Molecular dynamics, on the other hand, is a simulation technique that allows us to study the dynamic behavior of proteins and their interactions with other molecules over time. In recent years, the combination of these two methods has gained popularity in drug design as it can provide more accurate binding free energy predictions.
Computational techniques, Docking, Molecular dynamics, Protein-ligand complex
The free energy of binding is an important parameter in drug design as it determines the strength of the interaction between a ligand and its target protein. Accurate prediction of the binding free energy of ligands to their targets is critical for the design and optimization of drugs with high potency and selectivity. However, estimating the free energy of binding using computational methods is difficult due to the complex nature of protein-ligand interactions and the large number of possible conformations that protein-ligand complexes can adopt. It is still difficult .
Docking is a valuable tool for predicting the binding mode and affinity of a ligand for its target, but the dynamic behavior of proteins and ligands is not taken into account. This can lead to inaccurate predictions of binding free energies. Molecular dynamics simulations, on the other hand, can capture the dynamic behavior of proteins and ligands, but are computationally intensive and may not sample the entire conformational space of protein-ligand complexes .
To overcome these limitations, a combination of docking and molecular dynamics has been proposed as a powerful approach to accurately estimate binding free energies. In this approach, the binding mode and affinity of a ligand for its target are first predicted using docking. The resulting protein-ligand complex undergoes molecular dynamics simulations to sample the conformational space of the complex and obtain a more accurate estimate of the binding free energy .
Several studies have demonstrated the effectiveness of this approach in accurately predicting the binding free energy of protein-ligand complexes. For example, we predict the binding free energy of several inhibitors of the enzyme carbonic anhydrase II. The results showed that the combination of docking and molecular dynamics simulations yielded more accurate binding free energy predictions compared to docking alone .
An approach to study the binding of the drug molecule ciprofloxacin to its target protein, DNA gyrase. As a result, we find that combining docking and molecular dynamics simulations provides a better understanding of protein-ligand interactions and more accurately predicts binding free energies compared to docking alone .
A combination of docking and molecular dynamics simulations is a powerful approach for accurately estimating binding free energies in drug design. This approach provides valuable insight into protein-ligand interactions and aids in the design and optimization of drugs with high potency and selectivity. As computational resources continue to improve, this approach may become an increasingly valuable tool in drug discovery
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