Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go

By Nicolas Moitessier, Pablo Englebienne, D. Lee, J. Lawandi, Christopher R. Corbeil. 2008

481 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Pharmacology, Spectroscopy, Computational Biology & Bioinformatics

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Citations in the corpus

Molecular Docking and Structure-Based Drug Design Strategies (2015, background): “Covalent docking algorithms are aimed to explore the energy landscape available to the ligand when it is covalently linked to the receptor, as well as evaluate the binding energetics of the interaction [91].”
Virtual screening strategies in drug discovery: a critical review. (2013, background): “Scoring functions developed during the last few years have been reviewed in other works [67, 68].”
Structure-Based Virtual Screening for Drug Discovery: a Problem-Centric Review (2012, background): “Current scoring functions, as reviewed in other works (23,72), can be roughly classi ﬁ ed into three types: (a) Force ﬁ eld-based scoring functions employ classic force ﬁ eld to compute the noncovalent ligand – target interactions, such as van der Waals and electrostatic energies.”
Molecular docking: a powerful approach for structure-based drug discovery. (2011, methodology): “Considering the limitation of computer resources, docking has been performed with a flexible ligand and a rigid receptor for a long time, and remains the most popular method in use [7, 30-35].”
Docking and scoring with ICM: the benchmarking results and strategies for improvement (2012, background): “The reasons behind this scoring problem have been well described in recent reviews [2, 3], and typically pointed out to protein flexibility and induced-fit upon ligand binding [4], oversimplification of energy terms commonly used in the scoring functions such as solvation [5] and entropy contributio”
Key Topics in Molecular Docking for Drug Design (2019, background): “Those algorithms try to match the distances between each of the ligand’s and protein’s pharmacophoric points [29].”
A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking (2010, unknown): “Furthermore, this technique can also be applied to a range of structural bioinformatics problems, such as protein function prediction (Favia et al., 2008).”