TY - JOUR
T1 - Consensus virtual screening approaches to predict protein ligands
AU - Kukol, A.
N1 - 'This is the author's version of a work that was accepted for publication in European Journal of Medicinal Chemistry. Changes resulting from the publishing process, such as structural formatting and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in European Journal of Medicinal Chemistry, 46 (9) (2011) DOI 10.1016/j.ejmech.2011.05.026'
PY - 2011
Y1 - 2011
N2 - In order to exploit the advantages of receptor-based virtual screening, namely time/cost saving and specificity, it is important to rely on algorithms that predict a high number of active ligands at the top ranks of a small molecule database. Towards that goal consensus methods combining the results of several docking algorithms were developed and compared against the individual algorithms. Furthermore, a recently proposed rescoring method based on drug efficiency indices was evaluated. Among AutoDock Vina 1.0, AutoDock 4.2 and GemDock, AutoDock Vina was the best performing single method in predicting high affinity ligands from a database of known ligands and decoys. The rescoring of predicted binding energies with the water/octanol partition coefficient did not lead to an improvement averaged over ten receptor targets. Various consensus algorithms were investigated and a simple combination of AutoDock and AutoDock Vina results gave the most consistent performance that showed early enrichment of known ligands for all receptor targets investigated. In case a number of ligands is known for a specific target, every method proposed in this study should be evaluated.
AB - In order to exploit the advantages of receptor-based virtual screening, namely time/cost saving and specificity, it is important to rely on algorithms that predict a high number of active ligands at the top ranks of a small molecule database. Towards that goal consensus methods combining the results of several docking algorithms were developed and compared against the individual algorithms. Furthermore, a recently proposed rescoring method based on drug efficiency indices was evaluated. Among AutoDock Vina 1.0, AutoDock 4.2 and GemDock, AutoDock Vina was the best performing single method in predicting high affinity ligands from a database of known ligands and decoys. The rescoring of predicted binding energies with the water/octanol partition coefficient did not lead to an improvement averaged over ten receptor targets. Various consensus algorithms were investigated and a simple combination of AutoDock and AutoDock Vina results gave the most consistent performance that showed early enrichment of known ligands for all receptor targets investigated. In case a number of ligands is known for a specific target, every method proposed in this study should be evaluated.
KW - molecular docking
KW - in-silico screening
KW - consensus ranking
KW - benchmark
KW - comparison
UR - http://www.scopus.com/inward/record.url?scp=80052948931&partnerID=8YFLogxK
U2 - 10.1016/j.ejmech.2011.05.026
DO - 10.1016/j.ejmech.2011.05.026
M3 - Article
AN - SCOPUS:80052948931
SN - 0223-5234
VL - 46
SP - 4661
EP - 4664
JO - European Journal of Medicinal Chemistry
JF - European Journal of Medicinal Chemistry
IS - 9
ER -