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J Cheminform. 2016 Aug 10;8:39. doi: 10.1186/s13321-016-0151-5. eCollection 2016.

Large-scale ligand-based predictive modelling using support vector machines.

Author information

1
Department of Pharmaceutical Biosciences, Uppsala University, 751 24 Uppsala, Sweden.
2
Department of Pharmaceutical Biosciences, Uppsala University, 751 24 Uppsala, Sweden ; Science for Life Laboratory, Uppsala University, 751 24 Uppsala, Sweden.
3
Department of Medical Sciences, Uppsala University, 751 85 Uppsala, Sweden.

Abstract

The increasing size of datasets in drug discovery makes it challenging to build robust and accurate predictive models within a reasonable amount of time. In order to investigate the effect of dataset sizes on predictive performance and modelling time, ligand-based regression models were trained on open datasets of varying sizes of up to 1.2 million chemical structures. For modelling, two implementations of support vector machines (SVM) were used. Chemical structures were described by the signatures molecular descriptor. Results showed that for the larger datasets, the LIBLINEAR SVM implementation performed on par with the well-established libsvm with a radial basis function kernel, but with dramatically less time for model building even on modest computer resources. Using a non-linear kernel proved to be infeasible for large data sizes, even with substantial computational resources on a computer cluster. To deploy the resulting models, we extended the Bioclipse decision support framework to support models from LIBLINEAR and made our models of logD and solubility available from within Bioclipse.

KEYWORDS:

Bioclipse; Molecular signatures; Predictive modelling; QSAR; Support vector machine

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