Ligand binding plays an essential role in cellular signaling. Detailed understanding of the mechanisms, structures, thermodynamics, and kinetics of ligand binding is central to drug discovery in the pharmaceutical industry and academia. Despite the critical importance, such tasks remain challenging in computational chemistry and biophysics. Molecular docking has proven useful in rapid virtual screening of small molecules for drug discovery, although it is often difficult to fully incorporate receptor flexibility into ligand docking. Recent developments in the computing hardware and simulation algorithms have enabled molecular dynamics simulations to capture dynamic ligand binding and dissociation processes. These simulations can then be analyzed to compute both thermodynamic free energies and kinetic rates of ligand binding. In addition, Brownian dynamics simulations have been very efficient in generating a large number of ligand binding trajectories and estimate the binding kinetic rates. Finally, emerging machine learning techniques have greatly enhanced molecular simulations and facilitated analysis of the simulation trajectories. These remarkable advances are expected to tremendously expand our capabilities in ligand binding simulations for drug discovery.
This Research Topic aims to cover the latest advances in both method developments and applications of ligand binding studies using molecular simulations and machine learning approaches. We encourage insightful research studies on the structures, pathways, mechanisms, thermodynamic free energies, and kinetic rates of ligand binding to target receptors. Potential techniques used to address these problems include, but are not limited to, molecular docking, molecular dynamics, Brownian dynamics, and machine learning. Systems of our interest will involve ligand binding to any types of target molecules, including proteins, nucleic acids, and other soft materials.
We welcome expert researchers to submit Original Research, Brief Research reports, Methods, Reviews, Mini Reviews, Opinion, and Perspective articles in both method developments and applications of ligand binding studies, especially using molecular simulations and machine learning approaches. Areas to be covered in this Research Topic will include, but are not limited to the following:
• Ligand recognition by proteins, nucleic acids and any other soft materials.
• Structural predictions and binding conformations of ligands in target receptors.
• Pathways and mechanisms of ligand binding to target receptors.
• Thermodynamic free energy calculations of ligand binding.
• Binding and dissociation kinetic rate estimates of ligand binding.
Ligand binding plays an essential role in cellular signaling. Detailed understanding of the mechanisms, structures, thermodynamics, and kinetics of ligand binding is central to drug discovery in the pharmaceutical industry and academia. Despite the critical importance, such tasks remain challenging in computational chemistry and biophysics. Molecular docking has proven useful in rapid virtual screening of small molecules for drug discovery, although it is often difficult to fully incorporate receptor flexibility into ligand docking. Recent developments in the computing hardware and simulation algorithms have enabled molecular dynamics simulations to capture dynamic ligand binding and dissociation processes. These simulations can then be analyzed to compute both thermodynamic free energies and kinetic rates of ligand binding. In addition, Brownian dynamics simulations have been very efficient in generating a large number of ligand binding trajectories and estimate the binding kinetic rates. Finally, emerging machine learning techniques have greatly enhanced molecular simulations and facilitated analysis of the simulation trajectories. These remarkable advances are expected to tremendously expand our capabilities in ligand binding simulations for drug discovery.
This Research Topic aims to cover the latest advances in both method developments and applications of ligand binding studies using molecular simulations and machine learning approaches. We encourage insightful research studies on the structures, pathways, mechanisms, thermodynamic free energies, and kinetic rates of ligand binding to target receptors. Potential techniques used to address these problems include, but are not limited to, molecular docking, molecular dynamics, Brownian dynamics, and machine learning. Systems of our interest will involve ligand binding to any types of target molecules, including proteins, nucleic acids, and other soft materials.
We welcome expert researchers to submit Original Research, Brief Research reports, Methods, Reviews, Mini Reviews, Opinion, and Perspective articles in both method developments and applications of ligand binding studies, especially using molecular simulations and machine learning approaches. Areas to be covered in this Research Topic will include, but are not limited to the following:
• Ligand recognition by proteins, nucleic acids and any other soft materials.
• Structural predictions and binding conformations of ligands in target receptors.
• Pathways and mechanisms of ligand binding to target receptors.
• Thermodynamic free energy calculations of ligand binding.
• Binding and dissociation kinetic rate estimates of ligand binding.
