Comparison of Topological, Shape, and Docking Methods in Virtual Screening

By Georgia B. McGaughey, Robert P. Sheridan, C. Bayly, J. Culberson, Constantine Kreatsoulas, Stacey R. Lindsley, V. Maiorov, Jean-François Truchon, W. Cornell. Journal of Chemical Information and Modeling, 2007

375 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Organic Chemistry

View Paper: https://www.semanticscholar.org/paper/540cdb650d95962e9fe4a553b597bba12e5655e8

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Citations in the corpus

Effects of multiple conformers per compound upon 3-D similarity search and bioassay data analysis (2012, methodology): “Our previous study [10] attempted to address this question in part, by investigating the distributions of the Rapid Overlay of Chemical Structures (ROCS) [13-21]based 3-D similarity scores used in PubChem3D [8,10,11] between 270 billion unique compound pairs arising from 734,485 biologically tested ”
Molecular fingerprint similarity search in virtual screening. (2015, methodology): “In a different comparison, by McGaughey et al [66], the Daylight fingerprint was put to test against many other virtual screening methods, including protein-ligand docking.”
Integrating virtual screening and combinatorial chemistry for accelerated drug discovery. (2011, neutral): “[13, 17].”
Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go (2008, methodology): “, 2007) FLOG, FRED, Glide Native scoring functions (McGaughey et al., 2007) DOCK, AutoDock, GOLD Native scoring functions (Onodera et al.”
Virtual screening strategies in drug discovery: a critical review. (2013, methodology): “In recent years there has been increasing interest in combining SBVS methods with LBVS ones which, historically, have enjoyed more application and success than SBVS [121].”
Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets. (2017, methodology): “These machine learning methods are increasingly used for virtual screening of compounds which can enable more efficient utilization of high throughput screening (HTS) resources, by enriching the set of compounds screened with compounds that are active 44–47 .”
Comparison of Several Molecular Docking Programs: Pose Prediction and Virtual Screening Accuracy (2009, background): “14,76 Due to its functional form, it explicitly depends on the composition of the data set (i.e. the relative number of actives and decoys present).”
Comparing structural fingerprints using a literature-based similarity benchmark (2016, background): “A ligand-based virtual screen [7–11] tests the ability of a similarity measure to identify actives from within a set of decoys given a single active (or a small number of actives) as a query, with the underlying assumption that the actives are more structurally similar to each other than they are…”