A Classification Study of Respiratory Syncytial Virus (RSV) Inhibitors by Variable Selection with Random Forest

Experimental pEC50s for 216 selective respiratory syncytial virus (RSV) inhibitors are used to develop classification models as a potential screening tool for a large library of target compounds. Variable selection algorithm coupled with random forests (VS-RF) is used to extract the physicochemical features most relevant to the RSV inhibition. Based on the selected small set of descriptors, four other widely used approaches, i.e., support vector machine (SVM), Gaussian process (GP), linear discriminant analysis (LDA) and k nearest neighbors (kNN) routines are also employed and compared with the VS-RF method in terms of several of rigorous evaluation criteria. The obtained results indicate that the VS-RF model is a powerful tool for classification of RSV inhibitors, producing the highest overall accuracy of 94.34% for the external prediction set, which significantly outperforms the other four methods with the average accuracy of 80.66%. The proposed model with excellent prediction capacity from internal to external quality should be important for screening and optimization of potential RSV inhibitors prior to chemical synthesis in drug development

Faults Identification in Three-Phase Induction Motors Using Support Vector Machines

Induction motor is one of the most important motors used in industrial applications. The operating conditions may sometime lead the machine into different fault situations. The main types of external faults experienced by these motors are over loading, single phasing, unbalanced supply voltage, locked rotor, phase reversal, ground fault, under voltage and over voltage. The machine should be shut down when a fault is experienced to avoid damage and for the safety of the workers. Computer based relays monitor the machine and disconnect it during the faults. The relay logic used to identify these faults requires sophisticated signal processing techniques for fast and reliable operation. Artificial Intelligence (AI) techniques such as Artificial Neural Networks (ANN) have been applied in induction motor relays. Though the ANN based methods are reliable, the selection of the ANN structures and training is time consuming. Recently it is observed that the AI techniques such as Support Vector Machines (SVM) are alleviating some of the limitations of ANN method. The objectives of this study are to develop a SVM based induction motor external faults identifier and study its performance with real-time induction motor faults data. Data collected from a 1/3 hp, 208 V three-phase squirrel cage induction motor is used in this project. Radial Bases Function Kernel is used to train and test the SVM, though the effect of other Kernel functions was also studied. The proposed SVM method uses RMS values of three-phase voltages and currents as inputs. The testing results showed the efficacy of the SVM based method for identifying the external faults experienced by 3-phase induction motors. It is observed that the performance of the SVM based method is better than the ANN based method both in model creation and testing accuracy

Computer modeling of dapsone-mediated heteroactivation of flurbiprofen metabolism by CYP2C9

The occurrence of atypical kinetics in cytochrome P450 reactions can confound in vitro determinations of a drug\u27s kinetic parameters. During drug development, inaccurate kinetic parameter estimates can lead to incorrect decisions about a lead compound\u27s potential for success. It has become widely accepted that in certain CYP subfamilies more than one molecule can occupy the active site simultaneously, in some cases resulting in enhanced substrate turnover (heteroactivation). However, the specific mechanism(s) by which dual-compound binding results in heteroactivation remain unclear. It is known that orientation of the substrate in the active site, as dictated by interactions with active site residues, plays a large role in metabolic outcome. Effector compounds have been shown in vitro to alter substrate position in the active site. Here, data obtained via in silico methods including docking, molecular dynamics, semi-empirical and ab initio quantum mechanics indicate that direct interaction between effector and substrate can play a role in stabilizing the substrate in an alternative conformation conducive to oxidation. In this study a high-throughput screening computer model of heteroactivation of flurbiprofen metabolism by CYP2C9 has been developed for the purpose of elucidating key interactions between substrate, effector, and enzyme responsible for heteroactivation in this system, as well as to predict as yet unknown activators

Identification of structure activity relationships in primary screening data of high-throughput screening assays

The aim of the thesis was to identify structure activity relationships (SAR) in the primary screening data of high-throughput screening (HTS) assays. The strategy was to perform a hierarchical clustering of the molecules, assign the primary screening data to the created clusters and derive models from the clusters. The models should serve to identify singletons, clusters enriched with actives, not confirmed hits and false-negatives. Two hierarchical clustering algorithms, NIPALSTREE and hierarchical k-means have been developed and adapted for this purpose, respectively. A graphical user interface (GUI) has been implemented to extract SAR from the clustering results. Retrospective and prospective applications of the clustering approach were performed. SAR models were created by combining the clustering results with different chemoinformatic methods. NIPALSTREE projects a data set onto one dimension using principle component analysis. The data set is sorted according to the scoring vector and split at the median position into two subsets. The algorithm is applied recursively onto the subsets. The hierarchical k-means recursively separates a data set into two clusters using the k-means algorithm. Both algorithms are capable of clustering large data sets with more than a million data points. They were validated and compared to each other on the basis of different structural classes. NIPALSTREE provided with the loading vectors first insights into SAR whereas the hierarchical k-means yielded superior results. A GUI was developed allowing the display of and the navigation in the clustering results. Functionalities were integrated to analyse the clusters in the dendrogram, molecules in a cluster, and physicochemical properties of a molecule. Measures were developed to identify clusters enriched with actives, to characterize singletons and to analyse selectivity and specificity. Different protease inhibitors of the COBRA database were examined using the hierarchical k-means algorithm. Supported by similarity searches and nearest neighbour analyses thrombin inhibitor singletons were quickly isolated and displayed in the dendrogram. By scaling enrichment factors to the logarithm of the dendrogram level, clusters enriched with different structural classes of factor Xa inhibitors were simultaneously identified. The observed co-clustering of other protease inhibitors provided a deeper insight into selectivity and specificity and shows the utility of the approach for constructing focussed screening libraries. Specificity was analyzed by extracting and clustering relative frequencies of the protease inhibitors from the clusters of dendrogram level 7. A unique ligand based point of view on the pocketome of the protease enzymes was obtained. To identify not confirmed hits and false-negatives in the primary screening data of HTS assays, three assays were retrospectively analysed with the hierarchical k-means algorithm. A rule catalogue was developed judging hits in terminal clusters based on the cluster size, the percent control values of the entries in a cluster, the overall hit rate, the hit rate in the cluster and the environment of a cluster in the dendrogram. It resulted in the identification of a high proportion of not confirmed hits and provided for each hit a rating in context of related non-hits. This allows prioritizing compounds for follow-up studies. Non-hits and hits were retrieved from terminal clusters containing hits. Molecules bearing false-negative scaffolds were co-extracted and enriched. To minimize the number of false-positives in the extracted lists, Bayesian regularized artificial neutral network classification models were trained with the data. Applying the models marked improvement of enrichment factors for the false-negatives was obtained. It proofs the scaffold-hopping potential of the approach. NIPALSTREE, the hierarchical k-means algorithm and self-organising maps were prospectively applied to identify novel lead candidates for dopamine D3 receptors. Compounds with novel scaffolds and low nanomolar binding affinity (65 nM, compound 42) were identified. To provide a deeper insight into the SAR of these molecules, different alternative computational methods were employed. Support vector-based regression and partial least squares were examined. Predictive models for dopamine D2 and D3 receptor binding affinity values were obtained. Important features explaining SAR were extracted from the models. The prospective application of the models to the diverse and novel virtual screening data was of limited success only. Docking studies were performed using a homology model of the dopamine D3 receptor. The visual inspection of the binding modes resulted in the hypothesis of two alternative binding pockets for the aryl moiety of dopamine D3 receptor antagonists. A pharmacophore model was created simultaneously requiring both aryl moieties. Virtual screening with the model identified a nanomolar hit (65 nM, compound 59) corroborating the hypothesis of the two binding pockets and providing a new lead structure for dopamine D3 receptors. The presented data shows that the combined approach of hierarchically clustering a data set in combination with the subsequent usage of the clusters for model generation is suited to extract SAR from screening data. The models are successful in identifying singletons, clusters enriched with actives, not confirmed hits and false-negative scaffolds.Das Ziel der Arbeit war es, Struktur-Aktivitätsbeziehungen (SAR) in primären Screeningdaten von Hochdurchsatzscreening (HTS)- Assays zu finden. Als Strategie sollten die Moleküle hierarchisch geclustert werden, die primären Screeningdaten den gebildeten Clustern zugeordnet und Modelle aus den Clustern abgeleitet werden. Die Modelle sollten das Auffinden von Singletons, mit Hits angereicherter Cluster, nicht bestätigter Hits und falsch Negativer ermöglichen. Zu diesem Zweck wurden zwei hierarchische Clusteralgorithmen, NIPALSTREE und hierarchischer k-means, entwickelt bzw. angepasst. Eine graphische Benutzeroberfläche (GUI) wurde implementiert, um SAR aus den Ergebnissen der Clusterung abzuleiten. Retrospektive und prospektive Anwendungen wurden mit den Clusteransätzen verfolgt. SAR Modelle wurden durch Verwendung der Ergebnisse der Clusterung mit verschiedenen chemoinformatischen Verfahren erstellt. NIPALSTREE projiziert mit Hilfe der Hauptkomponentenanalyse einen Datensatz auf eine Dimension. Der Datensatz wird anhand des Scoringvektors sortiert und, basierend auf dem Median, in zwei Teilmengen aufgetrennt. Der Algorithmus wird rekursiv auf die neu gebildeten Mengen angewandt. Der hierarchische k-means Algorithmus trennt, basierend auf dem k-means Algorithmus, einen Datensatz rekursiv in zwei Cluster auf. Beide Algorithmen sind in der Lage, große Datenmengen mit mehr als einer Million Datenpunkte zu clustern. Sie wurden anhand verschiedener Strukturklassen validiert und miteinander verglichen. NIPALSTREE erbrachte mit dem Loadingvektor erste Einblicke in die SAR, wohingegen der hierarchische k-means zu besseren Ergebnissen führte. Eine GUI wurde entwickelt, die es erlaubt, die Clusterergebnisse darzustellen und darin zu navigieren. Funktionalitäten wurden bereitgestellt, um die Cluster im Dendrogramm, die Moleküle eines Clusters und die physikochemischen Eigenschaften eines Moleküls zu analysieren. Verfahren wurden entwickelt, um mit Hits angereicherte Cluster zu finden, Singletons zu charakterisieren und Selektivität und Spezifität zu analysieren. Verschiedene Proteaseinhibitoren aus der COBRA-Datenbank wurden mit dem hierarchischen k-means Algorithmus näher betrachtet. Mit Hilfe von Ähnlichkeitssuchen und nächsten Nachbaranalysen wurden Thrombininhibitorsingletons im Dendrogram in kürzester Zeit isoliert und dargestellt. Cluster, die mit verschiedenen Strukturklassen von Faktor-Xa-Inhibitoren angereichert waren, wurden, durch Skalierung des Anreicherungsfaktors auf den Logarithmus der Dendrogrammebene, gleichzeitig im Dendrogramm identifiziert. Eine Clusterung der Faktor-Xa-Inhibitoren mit anderen Proteaseinhibitoren wurde beobachtet. Sie erbrachte einen vertieften Einblick in Selektivität und Spezifität und zeigt die Anwendbarkeit des Ansatzes zur Erstellung fokussierter Screeningbibliotheken. Durch Extrahierung und Clusterung der relativen Anteile der Proteaseinhibitoren aus den Clustern von Dendrogrammebene sieben wurde die Spezifität der Proteaseinhibitoren analysiert. Eine spezifische, Liganden basierte Betrachtung des Pocketoms der Proteaseenzyme wurde erhalten. Um nicht bestätigte Hits und falsch Negative in den primären Screening Daten von HTS Assays zu finden, wurden drei Assays in Retrospektive mit dem hierarchischen k-means analysiert. Ein Regelwerk wurde entwickelt, welches Hits anhand der Clustergröße, des Prozent-Kontrollwertes der Einträge eines Clusters, der Gesamthitrate, der Hitrate in einem Cluster und der Umgebung des Clusters im Dendrogramm bewertet. Das Regelwerk führte zum Auffindung eines großen Anteils nicht bestätigter Hits. Zudem wurde für jeden Hit eine Bewertung im Kontext verwandter Nichthits erhalten. Dies erlaubt ein Priorisieren von Molekülen für Folgeuntersuchungen. Nichthits und Hits wurden aus Endcluster, die Hits enthielten, extrahiert. Moleküle mit falsch negativen Molekülgrundgerüsten wurden koextrahiert und angereichert. Um falsch Positive in den extrahierten Listen zu minimieren, wurden Bayesische regularisierte neuronale Klassifizierungsnetze mit den Daten trainiert. Die Anwendung der Modelle ergab eine deutliche Verbesserung der Anreicherungsfaktoren der falsch Negativen. Es zeigt, dass die Methode in der Lage ist, einen Molekülgrundgerüstwechsel durchzuführen. NIPALSTREE, der hierarchische k-means und selbst organisierende Karten wurden prospektiv angewandt, um neue Leitstrukturkandidaten für Dopamin-D3-Rezeptoren zu finden. Moleküle mit neuen Molekülgrundgerüsten und Bindungsaffinitäten im niedrigen nanomolaren Bereich wurden gefunden (65 nM für Molekül 42). Um einen tieferen Einblick in die SAR dieser Moleküle zu erhalten, wurden verschiede Computerverfahren verwendet. Supportvektorregression und PLS („partial least squares“) wurden untersucht. Es war möglich, voraussagende Modelle für Dopamin-D2 und D3 Bindungsaffinitäten zu erstellen. Die SAR erklärende Moleküleigenschaften konnten aus den Modellen extrahiert werden. Die prospektive Anwendung der Modelle auf die diversen und neuen virtuellen Screeningdaten war nur von begrenztem Erfolg. Dockingstudien wurden mit einem Homologiemodell des Dopamin-D3-Rezeptors durchgeführt. Die visuelle Begutachtung der Bindemoden führte zur Hypothese zweier alternativer Bindetaschen für den Aryl-Rest von Dopamin-D3-Rezeptorantagonisten. Ein Pharmakophormodell wurde erstellt, welches beide Aryl-Reste gleichzeitig benötigt. Ein virtuelles Screening mit dem Modell identifizierte einen nanomolaren Hit (65 nM für Molekül 59), welcher die Hypothese unterstützt und eine neue Leitstruktur für Dopamin-D3-Rezeptoren darstellt. Die vorgestellten Daten zeigen, dass der kombinierte Ansatz aus hierarchischer Clusterung und anschließender Verwendung der Cluster zur Modellerstellung, SAR in HTS-Daten findet. Die Modelle sind geeignet zum Auffinden von Singletons, mit Hits angereichter Cluster, nicht bestätigter Hits und falsch negativer Molekülgrundgerüste

Multivariate Prediction Models for Bio-Analytical Data

Quantitative bio-analytical techniques that enable parallel measurements of large numbers of biomolecules generate vast amounts of information for studying and characterising biological systems. These analytical methods are commonly referred to as omics technologies, and can be applied for measurements of e.g. mRNA transcript, protein or metabolite abundances in a biological sample. The work presented in this thesis focuses on the application of multivariate prediction models for modelling and analysis of biological data generated by omics technologies. Omics data commonly contain up to tens of thousands of variables, which are often both noisy and multicollinear. Multivariate statistical methods have previously been shown to be valuable for visualisation and predictive modelling of biological and chemical data with similar properties to omics data. In this thesis currently available multivariate modelling methods are used in new applications, and new methods are developed to address some of the specific challenges associated with modelling of biological data. Three closely related areas of multivariate modelling of biological data are described and demonstrated in this thesis. First, a multivariate projection method is used in a novel application for predictive modelling between omics data sets, demonstrating how data from two analytical sources can be integrated and modelled to- gether by exploring covariation patterns between the data sets. This approach is exemplified by modelling of data from two studies, the first containing proteomic and metabolic profiling data and the second containing transcriptomic and metabolic profiling data. Second, a method for piecewise multivariate modelling of short timeseries data is developed and demonstrated by modelling of simulated data as well as metabolic profiling data from a toxicity study, providing a new method for characterisation of multivariate bio-analytical time-series data. Third, a kernel-based method is developed and applied for non-linear multivariate prediction modelling of omics data, addressing the specific challenge of modelling non-linear variation in biological data