Computational methods for molecular docking

This tutorial was one of eight tutorials selected to be presented at the Third International Conference on Intelligent Systems for Molecular Biology which was held in the United Kingdom from July 16 to 19, 1995. Recently, it has been demonstrated that the knowledge of the three-dimensional structure of the protein can be used to derive new protein ligands with improved binding properties. This tutorial focuses on the following questions: What is its binding affinity toward a particular receptor? What are putative conformations of a ligand at the binding site? What are the similarities of different ligands in terms of their recognition capabilities? Where and in which orientation will a ligand bind to the active site? How is a new putative protein ligand selected? An overview is presented of the algorithms which are presently used to handle and predict protein-ligand interactions and to dock small molecule ligands into proteins

Quality-diversity in dissimilarity spaces

The theory of magnitude provides a mathematical framework for quantifying and maximizing diversity. We apply this framework to formulate quality-diversity algorithms in generic dissimilarity spaces. In particular, we instantiate and demonstrate a very general version of Go-Explore with promising performance.Comment: Minor bug fix: see new appendix J for details. Only small quantitative effects; no significant changes to results (but all redone

3D exemplar-based image inpainting in electron microscopy

In electron microscopy (EM) a common problem is the non-availability of data, which causes artefacts in reconstructions. In this thesis the goal is to generate artificial data where missing in EM by using exemplar-based inpainting (EBI). We implement an accelerated 3D version tailored to applications in EM, which reduces reconstruction times from days to minutes. We develop intelligent sampling strategies to find optimal data as input for reconstruction methods. Further, we investigate approaches to reduce electron dose and acquisition time. Sparse sampling followed by inpainting is the most promising approach. As common evaluation measures may lead to misinterpretation of results in EM and falsify a subsequent analysis, we propose to use application driven metrics and demonstrate this in a segmentation task. A further application of our technique is the artificial generation of projections in tiltbased EM. EBI is used to generate missing projections, such that the full angular range is covered. Subsequent reconstructions are significantly enhanced in terms of resolution, which facilitates further analysis of samples. In conclusion, EBI proves promising when used as an additional data generation step to tackle the non-availability of data in EM, which is evaluated in selected applications. Enhancing adaptive sampling methods and refining EBI, especially considering the mutual influence, promotes higher throughput in EM using less electron dose while not lessening quality.Ein häufig vorkommendes Problem in der Elektronenmikroskopie (EM) ist die Nichtverfügbarkeit von Daten, was zu Artefakten in Rekonstruktionen führt. In dieser Arbeit ist es das Ziel fehlende Daten in der EM künstlich zu erzeugen, was durch Exemplar-basiertes Inpainting (EBI) realisiert wird. Wir implementieren eine auf EM zugeschnittene beschleunigte 3D Version, welche es ermöglicht, Rekonstruktionszeiten von Tagen auf Minuten zu reduzieren. Wir entwickeln intelligente Abtaststrategien, um optimale Datenpunkte für die Rekonstruktion zu erhalten. Ansätze zur Reduzierung von Elektronendosis und Aufnahmezeit werden untersucht. Unterabtastung gefolgt von Inpainting führt zu den besten Resultaten. Evaluationsmaße zur Beurteilung der Rekonstruktionsqualität helfen in der EM oft nicht und können zu falschen Schlüssen führen, weswegen anwendungsbasierte Metriken die bessere Wahl darstellen. Dies demonstrieren wir anhand eines Beispiels. Die künstliche Erzeugung von Projektionen in der neigungsbasierten Elektronentomographie ist eine weitere Anwendung. EBI wird verwendet um fehlende Projektionen zu generieren. Daraus resultierende Rekonstruktionen weisen eine deutlich erhöhte Auflösung auf. EBI ist ein vielversprechender Ansatz, um nicht verfügbare Daten in der EM zu generieren. Dies wird auf Basis verschiedener Anwendungen gezeigt und evaluiert. Adaptive Aufnahmestrategien und EBI können also zu einem höheren Durchsatz in der EM führen, ohne die Bildqualität merklich zu verschlechtern

Algorithms for Triangles, Cones & Peaks

Three different geometric objects are at the center of this dissertation: triangles, cones and peaks. In computational geometry, triangles are the most basic shape for planar subdivisions. Particularly, Delaunay triangulations are a widely used for manifold applications in engineering, geographic information systems, telecommunication networks, etc. We present two novel parallel algorithms to construct the Delaunay triangulation of a given point set. Yao graphs are geometric spanners that connect each point of a given set to its nearest neighbor in each of $k$ cones drawn around it. They are used to aid the construction of Euclidean minimum spanning trees or in wireless networks for topology control and routing. We present the first implementation of an optimal $\mathcal{O}(n \log n)$-time sweepline algorithm to construct Yao graphs. One metric to quantify the importance of a mountain peak is its isolation. Isolation measures the distance between a peak and the closest point of higher elevation. Computing this metric from high-resolution digital elevation models (DEMs) requires efficient algorithms. We present a novel sweep-plane algorithm that can calculate the isolation of all peaks on Earth in mere minutes

Soft computing applied to optimization, computer vision and medicine

Artificial intelligence has permeated almost every area of life in modern society, and its significance continues to grow. As a result, in recent years, Soft Computing has emerged as a powerful set of methodologies that propose innovative and robust solutions to a variety of complex problems. Soft Computing methods, because of their broad range of application, have the potential to significantly improve human living conditions. The motivation for the present research emerged from this background and possibility. This research aims to accomplish two main objectives: On the one hand, it endeavors to bridge the gap between Soft Computing techniques and their application to intricate problems. On the other hand, it explores the hypothetical benefits of Soft Computing methodologies as novel effective tools for such problems. This thesis synthesizes the results of extensive research on Soft Computing methods and their applications to optimization, Computer Vision, and medicine. This work is composed of several individual projects, which employ classical and new optimization algorithms. The manuscript presented here intends to provide an overview of the different aspects of Soft Computing methods in order to enable the reader to reach a global understanding of the field. Therefore, this document is assembled as a monograph that summarizes the outcomes of these projects across 12 chapters. The chapters are structured so that they can be read independently. The key focus of this work is the application and design of Soft Computing approaches for solving problems in the following: Block Matching, Pattern Detection, Thresholding, Corner Detection, Template Matching, Circle Detection, Color Segmentation, Leukocyte Detection, and Breast Thermogram Analysis. One of the outcomes presented in this thesis involves the development of two evolutionary approaches for global optimization. These were tested over complex benchmark datasets and showed promising results, thus opening the debate for future applications. Moreover, the applications for Computer Vision and medicine presented in this work have highlighted the utility of different Soft Computing methodologies in the solution of problems in such subjects. A milestone in this area is the translation of the Computer Vision and medical issues into optimization problems. Additionally, this work also strives to provide tools for combating public health issues by expanding the concepts to automated detection and diagnosis aid for pathologies such as Leukemia and breast cancer. The application of Soft Computing techniques in this field has attracted great interest worldwide due to the exponential growth of these diseases. Lastly, the use of Fuzzy Logic, Artificial Neural Networks, and Expert Systems in many everyday domestic appliances, such as washing machines, cookers, and refrigerators is now a reality. Many other industrial and commercial applications of Soft Computing have also been integrated into everyday use, and this is expected to increase within the next decade. Therefore, the research conducted here contributes an important piece for expanding these developments. The applications presented in this work are intended to serve as technological tools that can then be used in the development of new devices

Kern-basierte Lernverfahren für das virtuelle Screening

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.Wir untersuchen moderne Kern-basierte maschinelle Lernverfahren für das Liganden-basierte virtuelle Screening. Insbesondere entwickeln wir einen neuen Graphkern auf Basis iterativer Graphähnlichkeit und optimaler Knotenzuordnungen, setzen die Kernhauptkomponentenanalyse für Projektionsfehler-basiertes Novelty Detection ein, und beschreiben die Entdeckung eines neuen selektiven Agonisten des Peroxisom-Proliferator-aktivierten Rezeptors gamma mit Hilfe von Gauß-Prozess-Regression. Virtuelles Screening ist die rechnergestützte Priorisierung von Molekülen bezüglich einer vorhergesagten Eigenschaft. Es handelt sich um ein Problem der Chemieinformatik, das in der Trefferfindungsphase der Medikamentenentwicklung auftritt. Seine Liganden-basierte Variante beruht auf dem Ähnlichkeitsprinzip, nach dem (strukturell) ähnliche Moleküle tendenziell ähnliche Eigenschaften haben. In unserer Beschreibung des Lösungsansatzes mit Kern-basierten Lernverfahren betonen wir die Bedeutung molekularer Repräsentationen, einschließlich der auf ihnen definierten (Un)ähnlichkeitsmaße. Wir untersuchen Effekte in hochdimensionalen chemischen Deskriptorräumen, ihre Auswirkungen auf Ähnlichkeits-basierte Verfahren und geben einen Literaturüberblick zu Empfehlungen zur retrospektiven Validierung, einschließlich eines Beispiel-Workflows. Graphkerne sind formale Ähnlichkeitsmaße, die inneren Produkten entsprechen und direkt auf Graphen, z.B. annotierten molekularen Strukturgraphen, definiert werden. Wir geben einen Literaturüberblick über Graphkerne, insbesondere solche, die auf zufälligen Irrfahrten, Subgraphen und optimalen Knotenzuordnungen beruhen. Indem wir letztere mit einem Ansatz zur iterativen Graphähnlichkeit kombinieren, entwickeln wir den iterative similarity optimal assignment Graphkern. Wir beschreiben einen iterativen Algorithmus, zeigen dessen Konvergenz sowie die Eindeutigkeit der Lösung, und geben eine obere Schranke für die Anzahl der benötigten Iterationen an. In einer retrospektiven Studie zeigte unser Graphkern konsistent bessere Ergebnisse als chemische Deskriptoren und andere, auf optimalen Knotenzuordnungen basierende Graphkerne. Chemische Datensätze liegen oft auf Mannigfaltigkeiten niedrigerer Dimensionalität als der umgebende chemische Deskriptorraum. Dimensionsreduktionsmethoden erlauben die Identifikation dieser Mannigfaltigkeiten und stellen dadurch deskriptive Modelle der Daten zur Verfügung. Für spektrale Methoden auf Basis der Kern-Hauptkomponentenanalyse ist der Projektionsfehler ein quantitatives Maß dafür, wie gut neue Daten von solchen Modellen beschrieben werden. Dies kann zur Identifikation von Molekülen verwendet werden, die strukturell unähnlich zu den Trainingsdaten sind, und erlaubt so Projektionsfehler-basiertes Novelty Detection für virtuelles Screening mit ausschließlich positiven Beispielen. Wir führen eine Machbarkeitsstudie zur Lernbarkeit des Konzepts von Fettsäuren durch die Hauptkomponentenanalyse durch. Der Peroxisom-Proliferator-aktivierte Rezeptor (PPAR) ist ein im Zellkern vorkommender Rezeptor, der den Fett- und Zuckerstoffwechsel reguliert. Er spielt eine wichtige Rolle in der Entwicklung von Krankheiten wie Typ-2-Diabetes und Dyslipidämie. Wir etablieren ein Gauß-Prozess-Regressionsmodell für PPAR gamma-Agonisten mit chemischen Deskriptoren und unserem Graphkern durch gleichzeitiges Lernen mehrerer Kerne. Das Screening einer kommerziellen Substanzbibliothek und die anschließende Testung 15 ausgewählter Substanzen in einem Zell-basierten Transaktivierungsassay ergab vier aktive Substanzen. Eine davon, ein Naturstoff mit Cyclobutan-Grundgerüst, ist ein voller selektiver PPAR gamma-Agonist (EC50 = 10 +/- 0,2 muM, inaktiv auf PPAR alpha und PPAR beta/delta bei 10 muM). Unsere Studie liefert einen neuen PPAR gamma-Agonisten, legt den Wirkmechanismus eines bioaktiven Naturstoffs offen, und erlaubt Rückschlüsse auf die Naturstoffursprünge von Pharmakophormustern in synthetischen Liganden

LIPIcs, Volume 274, ESA 2023, Complete Volume

LIPIcs, Volume 274, ESA 2023, Complete Volum