Finding more needles in the haystack: A simple and efficient method for improving high-throughput docking results.
The technology underpinning high-throughput docking (HTD) has developed over the past few years to where it has become a vital tool in modern drug discovery. Although the performance of various docking algorithms is adequate, the ability to accurately and consistently rank compounds using a scoring function remains problematic. We show that by employing a simple machine learning method (naïve Bayes) it is possible to significantly overcome this deficiency. Compounds from the Available Chemical Directory (ACD), along with known active compounds, were docked into two protein targets using three software packages. In cases where HTD alone was able to show some enrichment, the application of naïve Bayes was able to improve upon the enrichment. The application of this methodology to enrich HTD results can be carried out without a priori knowledge of the activity of compounds and results in superior enrichment of known actives compared to the use of scoring methods alone.
Klon
Anthony E
AE
Novartis Institute for Biomedical Research, 100 Technology Square, Cambridge, Massachusetts 02139, USA.
Glick
Meir
M
Thoma
Mathis
M
Acklin
Pierre
P
Davies
John W
JW
D001499:Bayes Theorem; D016208:Databases, Factual; D008024:Ligands; D011485:Protein Binding; D021281:Quantitative Structure-Activity Relationship; D012984:Software
D008024:Ligands