Virtual screening and fast automated docking methods.

By Gisbert Schneider, Hans-Joachim Böhm. Drug Discovery Today, 2002

349 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/388be440bf0087406409ddd43e6a5e718f67a148

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. Gisbert Schneider
2. N. V. Dokholyan
3. Alexander Tropsha
4. Shuangye Yin
5. Jan A. Hiss
6. Markus Hartenfeller
7. Robert E. W. Hancock
8. Christopher D. Fjell

In Molecular Biology

1. Gisbert Schneider
2. N. V. Dokholyan
3. Shuangye Yin
4. Alexander Tropsha
5. Christopher D. Fjell
6. Markus Hartenfeller
7. Jan A. Hiss
8. Robert E. W. Hancock

In Spectroscopy

1. N. V. Dokholyan
2. Shuangye Yin
3. Gisbert Schneider
4. Alexander Tropsha
5. Jan A. Hiss
6. Robert E. W. Hancock
7. Christopher D. Fjell
8. Elizabeth A. Proctor

Citations in the corpus

• A guide to drug discovery: Hit and lead generation: beyond high-throughput screening (2003, background): “In addition, biostructural information can also be applied if availabl”
• Enabling future drug discovery by de novo design (2011, background): “89 , 92 Atom pairs that occur more often than expected by chance result in negative interaction energies (attraction) while less frequently observed pairs score positive (repulsion).”
• Computer-based de novo design of drug-like molecules (2005, background): “Recent examples of de novo design There exist several published examples of successful de novo design projects, and comprehensive lists of applications have been compiled elsewher”
• Cheminformatics Meets Molecular Mechanics: A Combined Application of Knowledge-based Pose Scoring and Physical Force Field-based Hit Scoring Functions Improves the Accuracy of Structure-Based Virtual Screening (2011, neutral): “Beware of q(2)! J Mol Graphics Modell.”
• Designing antimicrobial peptides: form follows function (2011, methodology): “Virtual screening methods can be used when exhaustive synthesis and testing is prohibi­ tively expensive and biology­assisted techniques such as phage display 119 cannot be applied 120 .”
• Discrete molecular dynamics distinguishes nativelike binding poses from decoys in difficult targets. (2012, methodology): “Biophysical Journal 102(1) 144–151 pied by less favorably scoring poses is more sparsely sampled by the MedusaDock algorithm, highlighting the efficiency of the docking protocol.”
• An interaction-motif-based scoring function for protein-ligand docking (2010, background): “Background Drug design and discovery play a pivotal role in driving research in computational chemistry and biology [1-6].”
• ROSETTALIGAND: Protein–small molecule docking with full side‐chain flexibility (2006, neutral): “Currently, docking and screening appear to be best carried out with different methods: DOCK,(6) AUTODOCK,(7,8) FLEXX,(9) and GOLD(10) are widely used docking programs.(4,5,7,11,12) A wide variety of empirical scoring functions have been used for virtual screening, the best of which include XSCORE,(1”
• MedusaScore: An Accurate Force-Field Based Scoring Function for Virtual Drug Screening (2008, background): “By computational modeling of the target binding site, a ligand can be constructed de novo3 or via screening over a large database of millions of chemical compounds (virtual screening)4, 5.”