Understanding ligand-receptor non-covalent binding kinetics using molecular modeling.
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TLDR
This review provides an introduction to the kinetics of drug binding from a fundamental chemistry perspective and focuses on recent developments of computational tools and their applications to non-covalent binding kinetics.Abstract:
Kinetic properties may serve as critical differentiators and predictors of drug efficacy and safety, in addition to the traditionally focused binding affinity. However the quantitative structure-kinetics relationship (QSKR) for modeling and ligand design is still poorly understood. This review provides an introduction to the kinetics of drug binding from a fundamental chemistry perspective. We focus on recent developments of computational tools and their applications to non-covalent binding kinetics.read more
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Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development
Outi M. H. Salo-Ahen,Ida Alanko,Rajendra Bhadane,Alexandre M. J. J. Bonvin,Rodrigo V. Honorato,Shakhawath Hossain,André H. Juffer,Aleksei Kabedev,Maija Lahtela-Kakkonen,Anders S. Larsen,Eveline Lescrinier,Parthiban Marimuthu,Muhammad Usman Mirza,Ghulam Mustafa,Ariane Nunes-Alves,Ariane Nunes-Alves,Tatu Pantsar,Tatu Pantsar,Atefeh Saadabadi,Kalaimathy Singaravelu,Michiel Vanmeert +20 more
TL;DR: A broad overview of the current application possibilities of MD in drug discovery and pharmaceutical development is given, including how MD can be used in studying the crystalline and amorphous solids, the stability ofAmorphous drug or drug-polymer formulations, and drug solubility.
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Comparison Study of Computational Prediction Tools for Drug-Target Binding Affinities.
Maha A. Thafar,Arwa Bin Raies,Somayah Albaradei,Somayah Albaradei,Magbubah Essack,Vladimir B. Bajic +5 more
TL;DR: This study provides a comprehensive overview of the existing methods that predict drug-target binding affinities (DTBA) and focuses on the methods developed using artificial intelligence, machine learning, and deep learning approaches, as well as related benchmark datasets and databases.
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Potential Mean Force from Umbrella Sampling Simulations: What Can We Learn and What Is Missed?
TL;DR: For ligand-protein systems, these studies suggest that the dissociation trajectories modeled by an enhanced sampling method must maintain a natural molecular movement to avoid biased PMF plots when using US simulations.
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Binding kinetics of ligands acting at GPCRs
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Escape of a Small Molecule from Inside T4 Lysozyme by Multiple Pathways.
TL;DR: Molecular dynamics simulations were employed to model benzene exit from its binding cavity using the weighted ensemble (THE AUTHORS) approach to enhance sampling of low-probability unbinding trajectories and provide a semiquantitative characterization of pathway heterogeneity for binding of small molecules to proteins.
References
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