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We investigate the utility of modern kernel-based machine learning methods for ligand-based virtual screening. In particular, we introduce a new graph kernel based on iterative graph similarity and optimal assignments, apply kernel principle component analysis to projection error-based novelty detection, and discover a new selective agonist of the peroxisome proliferator-activated receptor gamma using Gaussian process regression. Virtual screening, the computational ranking of compounds with respect to a predicted property, is a cheminformatics problem relevant to the hit generation phase of drug development. Its ligand-based variant relies on the similarity principle, which states that (structurally) similar compounds tend to have similar properties. We describe the kernel-based machine learning approach to ligand-based virtual screening; in this, we stress the role of molecular representations, including the (dis)similarity measures defined on them, investigate effects in high-dimensional chemical descriptor spaces and their consequences for similarity-based approaches, review literature recommendations on retrospective virtual screening, and present an example workflow. Graph kernels are formal similarity measures that are defined directly on graphs, such as the annotated molecular structure graph, and correspond to inner products. We review graph kernels, in particular those based on random walks, subgraphs, and optimal vertex assignments. Combining the latter with an iterative graph similarity scheme, we develop the iterative similarity optimal assignment graph kernel, give an iterative algorithm for its computation, prove convergence of the algorithm and the uniqueness of the solution, and provide an upper bound on the number of iterations necessary to achieve a desired precision. In a retrospective virtual screening study, our kernel consistently improved performance over chemical descriptors as well as other optimal assignment graph kernels. Chemical data sets often lie on manifolds of lower dimensionality than the embedding chemical descriptor space. Dimensionality reduction methods try to identify these manifolds, effectively providing descriptive models of the data. For spectral methods based on kernel principle component analysis, the projection error is a quantitative measure of how well new samples are described by such models. This can be used for the identification of compounds structurally dissimilar to the training samples, leading to projection error-based novelty detection for virtual screening using only positive samples. We provide proof of principle by using principle component analysis to learn the concept of fatty acids. The peroxisome proliferator-activated receptor (PPAR) is a nuclear transcription factor that regulates lipid and glucose metabolism, playing a crucial role in the development of type 2 diabetes and dyslipidemia. We establish a Gaussian process regression model for PPAR gamma agonists using a combination of chemical descriptors and the iterative similarity optimal assignment kernel via multiple kernel learning. Screening of a vendor library and subsequent testing of 15 selected compounds in a cell-based transactivation assay resulted in 4 active compounds. One compound, a natural product with cyclobutane scaffold, is a full selective PPAR gamma agonist (EC50 = 10 +/- 0.2 muM, inactive on PPAR alpha and PPAR beta/delta at 10 muM). The study delivered a novel PPAR gamma agonist, de-orphanized a natural bioactive product, and, hints at the natural product origins of pharmacophore patterns in synthetic ligands.
For a virtual screening study, we introduce a combination of machine learning techniques, employing a graph kernel, Gaussian process regression and clustered cross-validation. The aim was to find ligands of peroxisome-proliferator activated receptor gamma (PPAR-y). The receptors in the PPAR family belong to the steroid-thyroid-retinoid superfamily of nuclear receptors and act as transcription factors. They play a role in the regulation of lipid and glucose metabolism in vertebrates and are linked to various human processes and diseases. For this study, we used a dataset of 176 PPAR-y agonists published by Ruecker et al. ...
Deichbau und andere flussbautechnische Maßnahmen haben dazu geführt, dass die Mittlere Elbe ihre ursprünglichen Überschwemmungsgebiete verloren hat. Um die Auswirkungen der alljährlich auftretenden Hochwasserereignisse einzudämmen, wurden große Bereiche der Talniederung durch Deiche vom Überflutungsgeschehen abgetrennt. Diese Eingriffe in den Naturhaushalt ermöglichten gleichfalls eine intensive ackerbauliche Nutzung oder eine hochwassersichere Bebauung der Auen. Die natürliche Auendynamik ist heute weitestgehend auf einen schmalen Bereich entlang der Elbe beschränkt. Hinter den Deichen sind die für die Elbeauen typischen Lebensräume von der lebenswichtigen Auendynamik abgeschnitten. Angepasste Auenarten und -lebensgemeinschaften treten zugunsten von Allerweltsarten zurück. Eine Wiederanbindung von Altauenbereichen an das Überflutungsgeschehen ist deshalb eine der vordringlichsten Maßnahmen zur Revitalisierung gefährdeter Auenlebensräume und stellt eine Chance dar, einen nachhaltigen und modernen Hochwasserschutz mit Naturschutzzielen zu verbinden. An der Elbe entspricht das aktuelle Hochwasserschutzsystem nicht den heutigen Anforderungen an den Hochwasserschutz. Um jedoch jederzeit auf mögliche große Hochwasserereignisse reagieren zu können, entstanden Anfang der 1990er Jahre in den Anliegerländern der Elbe zahlreiche Pläne für Deichrückverlegungen.