In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling

By Sean Ekins, Jordi Mestres, Bernard Testa. British Journal of Pharmacology, 2007

688 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology, Pharmacology

View Paper: https://www.semanticscholar.org/paper/4e0c9dc3ee27856080692fa98e1d07f667515733

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Citations in the corpus

Potential of quantum computing for drug discovery (2018, background): “Other descriptors are computed solely from the 2-D or 3-D topology of the molecule based on graph-theoretical methods [39].”
Can we really do computer-aided drug design? (2011, background): “For example, there are many ways to seek to identify a potent compound against a therapeutic target [4].”
Exploiting drug-disease relationships for computational drug repositioning (2011, background): “Molecular docking Molecular docking comprises a set of computational methods that aimed at discovering novel relationships between chemical ligands and targets through use of simulation and modeling of their direct physical interaction [27].”
In silico pharmacology for drug discovery: applications to targets and beyond (2007, methodology): “A fifth example of a complex property is the protein– ligand interaction and appropriate scoring functions for which several methods have been developed such as force fields, empirical and knowledge-based approaches (see also Ekins et al ., 2007).”
Computational Methods in Drug Discovery (2014, background): “2D descriptors can be computed solely from the constitution or topology of a molecule, whereas 3D descriptors are obtained from the 3D structure of the molecule (Ekins et al., 2007).”
The Toxicity Data Landscape for Environmental Chemicals (2008, methodology): “Chemical structure analysis can be used as part of the prioritization process, for instance, in predicting bioaccumulation potential (Meylan et al. 1999; Weisbrod et al. 2007) and fractional absorption (Ekins et al. 2007a, 2007b).”