Finding More Needles in the Haystack:  A Simple and Efficient Method for Improving High-Throughput Docking Results

Abstract

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.

Comparison of topological descriptors for similarity-based virtual screening using multiple bioactive reference structures

“…Thus, the average enrichment values for ECFP_4B at 1% are 42.3 and 43.5 for BKD and data fusion, respectively, with the corresponding 5% values being 13.1 and 13.5, respectively, demonstrating the utility of the methods discussed here for virtual screening purposes. Circular substructures of various sorts have been widely used for applications such as structure and substructure searching [21][22][23], constitutional symmetry [24], structure elucidation [25] and, most recently, probabilistic modelling of bioactivity where a full training-set is available [26,27]. The work reported here demonstrates that this type of fragment is also very well suited to virtual screening using multiple reference structures.…”

Computational Methods in Drug Discovery

“…2D and 3D QSAR can also be used to track docking errors. This method has been used by Novartis where a QSAR model is built from docking scores rather than observed activities, and this model is applied to that set to provide additional score weights for each compound (Klon et al, 2004).…”

An Accurate and Interpretable Bayesian Classification Model for Prediction of hERG Liability

“…[24,[27][28][29][30] In practice, the naive Bayes classifier has been demonstrated to outperform other sophisticated classifiers in text characterization and anti-spam filtering, and has recently started to be applied in drug discovery. [31][32][33] In this study, a traditional naive Bayes classification was carried out, in which each atom type contributed equally and objectively in determining the final class membership of a compound. The high ROC accuracy and enrichment performance indicated that highly correlated atom-type descriptors did not have a clear negative impact on the performance of the classifier.…”