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- Alvarsson, Jonathan, et al.
(författare)
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Benchmarking Study of Parameter Variation When Using Signature Fingerprints Together with Support Vector Machines
- 2014
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Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 54:11, s. 3211-3217
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Tidskriftsartikel (refereegranskat)abstract
- QSAR modeling using molecular signatures and support vector machines with a radial basis function is increasingly used for virtual screening in the drug discovery field. This method has three free parameters: C, ?, and signature height. C is a penalty parameter that limits overfitting, ? controls the width of the radial basis function kernel, and the signature height determines how much of the molecule is described by each atom signature. Determination of optimal values for these parameters is time-consuming. Good default values could therefore save considerable computational cost. The goal of this project was to investigate whether such default values could be found by using seven public QSAR data sets spanning a wide range of end points and using both a bit version and a count version of the molecular signatures. On the basis of the experiments performed, we recommend a parameter set of heights 0 to 2 for the count version of the signature fingerprints and heights 0 to 3 for the bit version. These are in combination with a support vector machine using C in the range of 1 to 100 and gamma in the range of 0.001 to 0.1. When data sets are small or longer run times are not a problem, then there is reason to consider the addition of height 3 to the count fingerprint and a wider grid search. However, marked improvements should not be expected.
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| 3. |
- Alvarsson, Jonathan, et al.
(författare)
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Large-scale ligand-based predictive modelling using support vector machines
- 2016
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Ingår i: Journal of Cheminformatics. - : Springer Science and Business Media LLC. - 1758-2946. ; 8
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Tidskriftsartikel (refereegranskat)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.
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| 4. |
- Alvarsson, Jonathan, 1981-
(författare)
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Ligand-based Methods for Data Management and Modelling
- 2015
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Drug discovery is a complicated and expensive process in the billion dollar range. One way of making the drug development process more efficient is better information handling, modelling and visualisation. The majority of todays drugs are small molecules, which interact with drug targets to cause an effect. Since the 1980s large amounts of compounds have been systematically tested by robots in so called high-throughput screening. Ligand-based drug discovery is based on modelling drug molecules. In the field known as Quantitative Structure–Activity Relationship (QSAR) molecules are described by molecular descriptors which are used for building mathematical models. Based on these models molecular properties can be predicted and using the molecular descriptors molecules can be compared for, e.g., similarity. Bioclipse is a workbench for the life sciences which provides ligand-based tools through a point and click interface. The aims of this thesis were to research, and develop new or improved ligand-based methods and open source software, and to work towards making these tools available for users through the Bioclipse workbench. To this end, a series of molecular signature studies was done and various Bioclipse plugins were developed.An introduction to the field is provided in the thesis summary which is followed by five research papers. Paper I describes the Bioclipse 2 software and the Bioclipse scripting language. In Paper II the laboratory information system Brunn for supporting work with dose-response studies on microtiter plates is described. In Paper III the creation of a molecular fingerprint based on the molecular signature descriptor is presented and the new fingerprints are evaluated for target prediction and found to perform on par with industrial standard commercial molecular fingerprints. In Paper IV the effect of different parameter choices when using the signature fingerprint together with support vector machines (SVM) using the radial basis function (RBF) kernel is explored and reasonable default values are found. In Paper V the performance of SVM based QSAR using large datasets with the molecular signature descriptor is studied, and a QSAR model based on 1.2 million substances is created and made available from the Bioclipse workbench.
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| 5. |
- Alvarsson, Jonathan, et al.
(författare)
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Ligand-Based Target Prediction with Signature Fingerprints
- 2014
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Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 54:10, s. 2647-2653
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Tidskriftsartikel (refereegranskat)abstract
- When evaluating a potential drug candidate it is desirable to predict target interactions in silico prior to synthesis in order to assess, e.g., secondary pharmacology. This can be done by looking at known target binding profiles of similar compounds using chemical similarity searching. The purpose of this study was to construct and evaluate the performance of chemical fingerprints based on the molecular signature descriptor for performing target binding predictions. For the comparison we used the area under the receiver operating characteristics curve (AUC) complemented with net reclassification improvement (NRI). We created two open source signature fingerprints, a bit and a count version, and evaluated their performance compared to a set of established fingerprints with regards to predictions of binding targets using Tanimoto-based similarity searching on publicly available data sets extracted from ChEMBL. The results showed that the count version of the signature fingerprint performed on par with well-established fingerprints such as ECFP. The count version outperformed the bit version slightly; however, the count version is more complex and takes more computing time and memory to run so its usage should probably be evaluated on a case-by-case basis. The NRI based tests complemented the AUC based ones and showed signs of higher power.
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| 7. |
- Andersen, M., et al.
(författare)
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Melanocortin 2, 3 and 4 Receptor Gene Expressions are Downregulated in CD8(+) T Cytotoxic Lymphocytes and CD19(+) B Lymphocytes in Rheumatoid Arthritis Responding to TNF- Inhibition
- 2017
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Ingår i: Scandinavian Journal of Immunology. - : John Wiley & Sons. - 0300-9475 .- 1365-3083. ; 86:1, s. 31-39
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Tidskriftsartikel (refereegranskat)abstract
- Melanocortin signalling in leucocyte subsets elicits anti-inflammatory and immune tolerance inducing effects in animal experimental inflammation. In man, however, the effects of melanocortin signalling in inflammatory conditions have scarcely been examined. We explored the differential reactions of melanocortin 1-5 receptors (MC1-5R) gene expressions in pathogenetic leucocyte subsets in rheumatoid arthritis (RA) to treatment with TNF- inhibitor adalimumab. Seven patients with active RA donated blood at start and at 3-month treatment. CD4(+) T helper (h) lymphocytes (ly), CD8(+) T cytotoxic (c) ly, CD19(+) B ly and CD14(+) monocytes were isolated, using immunomagnetic beads, total RNA extracted and reverse transcription quantitative polymerase chain reaction (RT-qPCR) performed. Fold changes in MC1-5R, Th1-, inflammatory- and regulatory cytokine gene expressions were assessed for correlation. Six patients responded to adalimumab treatment, while one patient was non-responder. In all lymphocyte subtypes, MC1-5R gene expressions decreased in responders and increased in the non-responder. In responders, decrease in MC2R, MC3R and MC4R gene expressions in CD8(+) Tc and CD19(+) B ly was significant. Fold change in MC1-5R and IFN gene expressions correlated significantly in CD8(+) Tc ly, while fold change in MC1R, MC3R and MC5R and IL-1 gene expressions correlated significantly in CD4(+) Th ly. Our results show regulation of MC2R, MC3R and MC4R gene expressions in CD8(+) Tc ly and CD19(+) B ly. The correlations between fold change in different MCRs and disease driving cytokine gene expressions in CD8(+) Tc ly and CD4(+) Th ly point at a central immune modulating function of the melanocortin system in RA.
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