Supervised MD (SuMD) is a computational method that allows the exploration of ligand–receptor recognition pathway investigations in a nanosecond (ns) time scale. It consists of the incorporation of a tabu-like supervision algorithm on the ligand–receptor approaching distance into a classic molecular dynamics (MD) simulation technique. In addition to speeding up the acquisition of the ligand–receptor trajectory, this implementation facilitates the characterization of multiple binding events (such as meta-binding, allosteric, and orthosteric sites) by taking advantage of the all-atom MD simulations accuracy of a GPCR–ligand complex embedded into explicit lipid–water environment
The recent paradigm shift toward the use of the kinetics parameters in place of thermodynamic consta...
Exploring at the molecular level, all possible ligand–protein approaching pathways and, consequently...
Exploring at the molecular level, all possible ligand–protein approaching pathways and, consequently...
Supervised MD (SuMD) is a computational method that allows the exploration of ligand–receptor recogn...
Supervised MD (SuMD) is a computational method that allows the exploration of ligand–receptor recogn...
Supervised MD (SuMD) is a computational method that allows the exploration of ligand–receptor recogn...
Supervised MD (SuMD) is a computational method that allows the exploration of ligand–receptor recogn...
Supervised MD (SuMD) is a computational method that allows the exploration of ligand–receptor recogn...
Supervised MD (SuMD) is a computational method that allows the exploration of ligand–receptor recogn...
Molecular recognition is a crucial issue when aiming to interpret the mechanism of known active subs...
Molecular recognition is a crucial issue when aiming to interpret the mechanism of known active subs...
Molecular recognition is a crucial issue when aiming to interpret the mechanism of known active subs...
Supervised MD (SuMD) is a computational method that enables the exploration of ligand-receptor recog...
The recent paradigm shift toward the use of the kinetics parameters in place of thermodynamic consta...
During the last decades, the technological evolution has been very fast and has paved the way to a w...
The recent paradigm shift toward the use of the kinetics parameters in place of thermodynamic consta...
Exploring at the molecular level, all possible ligand–protein approaching pathways and, consequently...
Exploring at the molecular level, all possible ligand–protein approaching pathways and, consequently...
Supervised MD (SuMD) is a computational method that allows the exploration of ligand–receptor recogn...
Supervised MD (SuMD) is a computational method that allows the exploration of ligand–receptor recogn...
Supervised MD (SuMD) is a computational method that allows the exploration of ligand–receptor recogn...
Supervised MD (SuMD) is a computational method that allows the exploration of ligand–receptor recogn...
Supervised MD (SuMD) is a computational method that allows the exploration of ligand–receptor recogn...
Supervised MD (SuMD) is a computational method that allows the exploration of ligand–receptor recogn...
Molecular recognition is a crucial issue when aiming to interpret the mechanism of known active subs...
Molecular recognition is a crucial issue when aiming to interpret the mechanism of known active subs...
Molecular recognition is a crucial issue when aiming to interpret the mechanism of known active subs...
Supervised MD (SuMD) is a computational method that enables the exploration of ligand-receptor recog...
The recent paradigm shift toward the use of the kinetics parameters in place of thermodynamic consta...
During the last decades, the technological evolution has been very fast and has paved the way to a w...
The recent paradigm shift toward the use of the kinetics parameters in place of thermodynamic consta...
Exploring at the molecular level, all possible ligand–protein approaching pathways and, consequently...
Exploring at the molecular level, all possible ligand–protein approaching pathways and, consequently...