Bridging Molecular Docking to Molecular Dynamics in Exploring Ligand-Protein Recognition Process: An Overview.
Veronica Salmaso,Stefano Moro +1 more
TLDR
An overview of the evolution of structure-based drug discovery techniques in the study of ligand-target recognition phenomenon, going from the static molecular docking toward enhanced molecular dynamics strategies is presented.Abstract:
Computational techniques have been applied in the drug discovery pipeline since the 1980s. Given the low computational resources of the time, the first molecular modeling strategies relied on a rigid view of the ligand-target binding process. During the years, the evolution of hardware technologies has gradually allowed simulating the dynamic nature of the binding event. In this work, we present an overview of the evolution of structure-based drug discovery techniques in the study of ligand-target recognition phenomenon, going from the static molecular docking towards enhanced molecular dynamics strategies.read more
Citations
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Molecular Docking: Shifting Paradigms in Drug Discovery.
Luca Pinzi,Giulio Rastelli +1 more
TL;DR: This review describes how molecular docking was firstly applied to assist in drug discovery tasks, and illustrates newer and emergent uses and applications of docking, including prediction of adverse effects, polypharmacology, drug repurposing, and target fishing and profiling.
Journal ArticleDOI
Structural variations in human ACE2 may influence its binding with SARS-CoV-2 spike protein.
Mushtaq Hussain,Nusrat Jabeen,Fozia Raza,Sanya Shabbir,Sanya Shabbir,Ayesha Ashraf Baig,Anusha Amanullah,Basma Aziz +7 more
TL;DR: The data provide a structural basis of potential resistance against SARS‐CoV‐2 infection driven by ACE2 allelic variants.
Journal ArticleDOI
Discovering Anti-Cancer Drugs via Computational Methods.
TL;DR: The different subareas of the computer-aided drug discovery process with a focus on anticancer drugs are discussed and fruitful insights are provided into the area of cancer therapy.
Journal ArticleDOI
Protein–ligand binding with the coarse-grained Martini model
Paulo C. T. Souza,Sebastian Thallmair,Paolo Conflitti,Carlos Ramírez-Palacios,Riccardo Alessandri,Stefano Raniolo,Vittorio Limongelli,Vittorio Limongelli,Siewert J. Marrink +8 more
TL;DR: An approach to use the recently re-parametrized coarse-grained Martini model to perform unbiased millisecond sampling of protein-ligand binding interactions of small drug-like molecules and achieves high accuracy without the need of any a priori knowledge of binding pockets or pathways.
Journal ArticleDOI
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.
References
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TL;DR: It is shown that both the traditional and Lamarckian genetic algorithms can handle ligands with more degrees of freedom than the simulated annealing method used in earlier versions of AUTODOCK, and that the Lamarckia genetic algorithm is the most efficient, reliable, and successful of the three.
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