title: Large-scale ligand-based predictive modelling using support vector machines.
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.
authors: Department of Pharmaceutical Biosciences, Uppsala University, 751 24 Uppsala, Sweden.
authors: Jonathan
authors: J
authors: Alvarsson
authors: Department of Pharmaceutical Biosciences, Uppsala University, 751 24 Uppsala, Sweden.
authors: Samuel
authors: S
authors: Lampa
authors: Department of Pharmaceutical Biosciences, Uppsala University, 751 24 Uppsala, Sweden ; Science for Life Laboratory, Uppsala University, 751 24 Uppsala, Sweden.
authors: Wesley
authors: W
authors: Schaal
authors: Department of Medical Sciences, Uppsala University, 751 85 Uppsala, Sweden.
authors: Claes
authors: C
authors: Andersson
authors: Department of Pharmaceutical Biosciences, Uppsala University, 751 24 Uppsala, Sweden.
authors: Jarl E S
authors: JE
authors: Wikberg
authors: Department of Pharmaceutical Biosciences, Uppsala University, 751 24 Uppsala, Sweden ; Science for Life Laboratory, Uppsala University, 751 24 Uppsala, Sweden.
authors: Ola
authors: 0000-0002-8083-2864
authors: O
authors: Spjuth
keywords: Bioclipse; Molecular signatures; Predictive modelling; QSAR; Support vector machine