Abstract
BackgroundSupport Vector Machine has become one of the most popular machine learning tools used in virtual screening campaigns aimed at finding new drug candidates. Although it can be extremely effective in finding new potentially active compounds, its application requires the optimization of the hyperparameters with which the assessment is being run, particularly the C and gamma values. The optimization requirement in turn, establishes the need to develop fast and effective approaches to the optimization procedure, providing the best predictive power of the constructed model.Results In this study, we investigated the Bayesian and random search optimization of Support Vector Machine hyperparameters for classifying bioactive compounds. The effectiveness of these strategies was compared with the most popular optimization procedures—grid search and heuristic choice. We demonstrated that Bayesian optimization not only provides better, more efficient classification but is also much faster—the number of iterations it required for reaching optimal predictive performance was the lowest out of the all tested optimization methods. Moreover, for the Bayesian approach, the choice of parameters in subsequent iterations is directed and justified; therefore, the results obtained by using it are constantly improved and the range of hyperparameters tested provides the best overall performance of Support Vector Machine. Additionally, we showed that a random search optimization of hyperparameters leads to significantly better performance than grid search and heuristic-based approaches.ConclusionsThe Bayesian approach to the optimization of Support Vector Machine parameters was demonstrated to outperform other optimization methods for tasks concerned with the bioactivity assessment of chemical compounds. This strategy not only provides a higher accuracy of classification, but is also much faster and more directed than other approaches for optimization. It appears that, despite its simplicity, random search optimization strategy should be used as a second choice if Bayesian approach application is not feasible.Graphical abstractThe improvement of classification accuracy obtained after the application of Bayesian approach to the optimization of Support Vector Machines parameters.Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-015-0088-0) contains supplementary material, which is available to authorized users.
Highlights
Support Vector Machine has become one of the most popular machine learning tools used in virtual screening campaigns aimed at finding new drug candidates
Bayesian optimization [5, 6] and random search-based selection [8] have become more popular [9, 10]. As those approaches were not explored so far in the field of cheminformatics, we analyze their impact on classification accuracy and, more importantly, the speed and ease of use, that these approaches have lent to the optimization of Support Vector Machine (SVM) hyperparameters in the search for bioactive compounds
The SVMlight and libSVM were definitely the least effective methods of SVM usage; they did not provide the highest accuracy values for any of the target/ fingerprint combinations. This result is an obvious consequence of the fact that SVMlight and libSVM are just basic heuristics and their results cannot be comparable with any hyperparameters optimization technique
Summary
Support Vector Machine has become one of the most popular machine learning tools used in virtual screening campaigns aimed at finding new drug candidates It can be extremely effective in finding new potentially active compounds, its application requires the optimization of the hyperparameters with which the assessment is being run, the C and γ values. Czarnecki et al J Cheminform (2015) 7:38 the Support Vector Machine (SVM) It has a potential of providing very high VS performance, its application requires the optimization of the parameters used during the training process, which was proved to be crucial for obtaining accurate predictions. As those approaches were not explored so far in the field of cheminformatics, we analyze their impact on classification accuracy and, more importantly, the speed and ease of use, that these approaches have lent to the optimization of SVM hyperparameters in the search for bioactive compounds
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