Virtual screening of bioassay data

Amanda C Schierz

doi:10.1186/1758-2946-1-21

Abstract

BackgroundThere are three main problems associated with the virtual screening of bioassay data. The first is access to freely-available curated data, the second is the number of false positives that occur in the physical primary screening process, and finally the data is highly-imbalanced with a low ratio of Active compounds to Inactive compounds. This paper first discusses these three problems and then a selection of Weka cost-sensitive classifiers (Naive Bayes, SVM, C4.5 and Random Forest) are applied to a variety of bioassay datasets.ResultsPharmaceutical bioassay data is not readily available to the academic community. The data held at PubChem is not curated and there is a lack of detailed cross-referencing between Primary and Confirmatory screening assays. With regard to the number of false positives that occur in the primary screening process, the analysis carried out has been shallow due to the lack of cross-referencing mentioned above. In six cases found, the average percentage of false positives from the High-Throughput Primary screen is quite high at 64%. For the cost-sensitive classification, Weka's implementations of the Support Vector Machine and C4.5 decision tree learner have performed relatively well. It was also found, that the setting of the Weka cost matrix is dependent on the base classifier used and not solely on the ratio of class imbalance.ConclusionsUnderstandably, pharmaceutical data is hard to obtain. However, it would be beneficial to both the pharmaceutical industry and to academics for curated primary screening and corresponding confirmatory data to be provided. Two benefits could be gained by employing virtual screening techniques to bioassay data. First, by reducing the search space of compounds to be screened and secondly, by analysing the false positives that occur in the primary screening process, the technology may be improved. The number of false positives arising from primary screening leads to the issue of whether this type of data should be used for virtual screening. Care when using Weka's cost-sensitive classifiers is needed - across the board misclassification costs based on class ratios should not be used when comparing differing classifiers for the same dataset.

Highlights

There are three main problems associated with the virtual screening of bioassay data
The datasets are from the differing types of screening that can be performed using High-Throughput Screening (HTS) technology and they have varying sizes and minority classes. 21 datasets were created from the screening data
This section first looks at the setting of the Weka cost matrix and compares the misclassification costs needed for each classifier for each dataset

Summary

Introduction

There are three main problems associated with the virtual screening of bioassay data. In HTS, batches of compounds are screened against a biological target (bioassay) to test the compound's ability to bind to the target - if the compound binds it is an active for that target and known as a hit. Protein-based methods are employed when the 3D structure of the bioassay target is known and computational techniques involve the docking (virtual binding), and subsequent scoring, of candidate ligands (the part of the compound that is capable of binding) to the protein target. If a few active compounds are known structure-similarity techniques may be used; if the activity of several compounds is known discriminant analysis techniques, such as machine learning approaches, may be applied This is achieved by choosing several compounds that have known activity for a specific biological target and building predictive models that can discriminate between the active and inactive compounds.

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Results

Discussion

Conclusion