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WIREs Comput Mol Sci
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Structure based virtual screening: Fast and slow

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Abstract For many decades virtual screening methods have provided a convenient and cost effective in silico solution in the early stages of drug discovery. In particular, molecular docking uses structural information to approximate protein–ligand recognition, providing valuable information for large chemical libraries at a fast pace with multiple success stories to validate the approach. Nevertheless, fast turnaround of results required assumptions and approximations which compromise the accuracy of these algorithms. On the other side of the spectrum, physical‐based molecular simulations offer more precise and realistic models of protein–ligand binding at the cost of being slower and requiring more expensive computing infrastructure. Both fast and slow approaches are useful and solve different aspects of the same problem. Here, we aim to review these approaches focusing on their capabilities, context of usage and limitations, presenting multiple examples along the way. This article is categorized under: Molecular and Statistical Mechanics > Molecular Mechanics Software > Molecular Modeling Structure and Mechanism > Computational Biochemistry and Biophysics
An example of a large change in the conformation of a pocket. Abl tyrosine kinase can adopt two dramatically different pocket conformations named DFG‐in and DFG‐out. In the DFG‐out conformation (top left panel, DFG motif is shown in bright blue cartoon; PDBid: 3KFA), an extra allosteric pocket next to the main cavity becomes accessible, and the ligand B91 (purple licorice) can fit into it, while the DFG‐in conformation (bottom left panel, DFG motif in red cartoon; PDBid: 3KF4) does not. On the right panel, zoomed in overlapped structures show that the purple ligand (B91) clashes with phenylalanine (F382) in the DFG motif in the DFG‐in conformation98
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CELPP competition results as of February 25th, 2021. A set of automated docking protocols (X‐axis) are challenged weekly to predict the binding mode of several ligands to a target. Y‐axis reports the distribution of the RMSD values for the predicted poses. The number of attempted docking exercises for each protocol is reported next to its name, inside parenthesis
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Opening of a small cryptic pocket in GTPase KRas with CrypticScout. The binding of the benzene probe disrupts the contacts between three residues (Gln, Met, Tyr) and opens the cavity. (PDB code: 4L8G123)
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Structure and Mechanism > Computational Biochemistry and Biophysics
Software > Molecular Modeling
Molecular and Statistical Mechanics > Molecular Mechanics

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