Benchmarking sets for molecular docking.

By Niu Huang, Brian K. Shoichet, John J. Irwin. 2006

1,248 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Organic Chemistry

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Brian K. Shoichet
2. Jean-Louis Reymond
3. John J. Irwin
4. Ruud Van Deursen
5. Lorenz C. Blum
6. Teague Sterling
7. Ryan G. Coleman
8. Michael Carchia

In Organic Chemistry

1. Brian K. Shoichet
2. Jean-Louis Reymond
3. Mark McGann
4. R. Quiroga
5. Marcos A. Villarreal
6. Anthony Nicholls
7. John J. Irwin
8. Ruud Van Deursen

Citations in the corpus

• Ligand Pose and Orientational Sampling in Molecular Docking (2013, methodology): “Cofactor parameters were taken from previous versions of DUD[103] and DUD-E[87] as is, except for the parameters for the COMT cofactor S-adenosylmethionine, which were modified to properly protonate the sulfur moeity.”
• Machine-learning methods for ligand-protein molecular docking. (2021, background): “• Directory of Useful Decoys (DUD) [44] and DUD-E (for Enhanced) [45] contains 40 (resp.”
• Comparing structural fingerprints using a literature-based similarity benchmark (2016, background): “It is the union of three distinct datasets: 50 targets from ChEMBL 14, 21 from the Directory of Useful Decoys [34] (DUD) and 17 from the Maximum Unbiased Validation (MUV) dataset [35].”
• Ligand Discovery from a Dopamine D3 Receptor Homology Model and Crystal Structure (2011, background): “Up to 1,300 known dopaminergic ligands, along with up to 110,000 property-matched decoys, were docke”
• Vinardo: A Scoring Function Based on Autodock Vina Improves Scoring, Docking, and Virtual Screening (2016, methodology): “As a further measure of the virtual screening capabilities of Dk_scoring, Vina, and Vinardo, we performed a virtual screening of the DUD library [21] using docking.”
• Boosting Docking-Based Virtual Screening with Deep Learning (2016, methodology): “We used the Directory of Useful Decoys (DUD) 34 as a benchmark to evaluate our deep-learning-based virtual screening approach.”
• Automated Docking Screens: A Feasibility Study (2009, methodology): “In parallel, property-matched decoys for the ligand are found from ZINC using the DUD protocols 27,40 for enrichment and ranking.”