IMPROVING MOLECULAR FINGERPRINT SIMILARITY VIA ENHANCED FOLDING

Drug discovery depends on scientists finding similarity in molecular fingerprints to the drug target. A new way to improve the accuracy of molecular fingerprint folding is presented. The goal is to alleviate a growing challenge due to excessively long fingerprints. This improved method generates a new shorter fingerprint that is more accurate than the basic folded fingerprint. Information gathered during preprocessing is used to determine an optimal attribute order. The most commonly used blocks of bits can then be organized and used to generate a new improved fingerprint for more optimal folding. We thenapply the widely usedTanimoto similarity search algorithm to benchmark our results. We show an improvement in the final results using this method to generate an improved fingerprint when compared against other traditional folding methods

Software for supporting large scale data processing for High Throughput Screening

High Throughput Screening for is a valuable data generation technique for data driven knowledge discovery. Because the rate of data generation is so great, it is a challenge to cope with the demands of post experiment data analysis. This thesis presents three software solutions that I implemented in an attempt to alleviate this problem. The first is K-Screen, a Laboratory Information Management System designed to handle and visualize large High Throughput Screening datasets. K-Screen is being successfully used by the University of Kansas High Throughput Screening Laboratory to better organize and visualize their data. The next two algorithms are designed to accelerate the search times for chemical similarity searches using 1-dimensional fingerprints. The first algorithm balances information content in bit strings to attempt to find more optimal ordering and segmentation patterns for chemical fingerprints. The second algorithm eliminates redundant pruning calculations for large batch chemical similarity searches and shows a 250% improvement for the fastest current fingerprint search algorithm for large batch queries

Application and Development of Computational Methods for Ligand-Based Virtual Screening

The detection of novel active compounds that are able to modulate the biological function of a target is the primary goal of drug discovery. Different screening methods are available to identify hit compounds having the desired bioactivity in a large collection of molecules. As a computational method, virtual screening (VS) is used to search compound libraries in silico and identify those compounds that are likely to exhibit a specific activity. Ligand-based virtual screening (LBVS) is a subdiscipline that uses the information of one or more known active compounds in order to identify new hit compounds. Different LBVS methods exist, e.g. similarity searching and support vector machines (SVMs). In order to enable the application of these computational approaches, compounds have to be described numerically. Fingerprints derived from the two-dimensional compound structure, called 2D fingerprints, are among the most popular molecular descriptors available. This thesis covers the usage of 2D fingerprints in the context of LBVS. The first part focuses on a detailed analysis of 2D fingerprints. Their performance range against a wide range of pharmaceutical targets is globally estimated through fingerprint-based similarity searching. Additionally, mechanisms by which fingerprints are capable of detecting structurally diverse active compounds are identified. For this purpose, two different feature selection methods are applied to find those fingerprint features that are most relevant for the active compounds and distinguish them from other compounds. Then, 2D fingerprints are used in SVM calculations. The SVM methodology provides several opportunities to include additional information about the compounds in order to direct LBVS search calculations. In a first step, a variant of the SVM approach is applied to the multi-class prediction problem involving compounds that are active against several related targets. SVM linear combination is used to recover compounds with desired activity profiles and deprioritize compounds with other activities. Then, the SVM methodology is adopted for potency-directed VS. Compound potency is incorporated into the SVM approach through potencyoriented SVM linear combination and kernel function design to direct search calculations to the preferential detection of potent hit compounds. Next, SVM calculations are applied to address an intrinsic limitation of similarity-based methods, i.e., the presence of similar compounds having large differences in their potency. An especially designed SVM approach is introduced to predict compound pairs forming such activity cliffs. Finally, the impact of different training sets on the recall performance of SVM-based VS is analyzed and caveats are identified

Aspects of Metric Spaces in Computation

Metric spaces, which generalise the properties of commonly-encountered physical and abstract spaces into a mathematical framework, frequently occur in computer science applications. Three major kinds of questions about metric spaces are considered here: the intrinsic dimensionality of a distribution, the maximum number of distance permutations, and the difficulty of reverse similarity search. Intrinsic dimensionality measures the tendency for points to be equidistant, which is diagnostic of high-dimensional spaces. Distance permutations describe the order in which a set of fixed sites appears while moving away from a chosen point; the number of distinct permutations determines the amount of storage space required by some kinds of indexing data structure. Reverse similarity search problems are constraint satisfaction problems derived from distance-based index structures. Their difficulty reveals details of the structure of the space. Theoretical and experimental results are given for these three questions in a wide range of metric spaces, with commentary on the consequences for computer science applications and additional related results where appropriate

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Graphical and Topological Analysis of the Cell Nucleus

It is well known that our genetic material influences our tendency to develop certain conditions. Finding the causes behind these predispositions assumes the understanding of mechanisms handling and maintaining the genome. While the problem is important from the biological point of view, being one of the basic riddles of life, it also poses interesting questions which may only be answered by physics. Topics include transport, reaction-diffusion, polymer physics, equilibrium and non-equilibrium dynamics and chaos, amongst others. Experimental techniques, like microscopy or molecular biology approaches provide an ever improving insight in the structure of the nucleus, however, computational and modelling approaches are still needed to explain unknown aspects of genetics. % Evidence is accumulating that our genetic material not only influences our resemblance to relatives and the chances that we may have a tendency to develop certain diseases, but also our predisposition to contract viral infections or to develop conditions like depression, obesity or substance dependence. It has become clear that understanding how the genetic material is organized and how it is being handled might be the key to revolutionize medicine. Experimental techniques, like microscopy or molecular biology approaches provide an ever improving insight in the structure of the nucleus, however, computational and modelling approaches are still needed to explain unknown aspects of genetics. In this thesis we tackle the problem of understanding the structure of the nucleus from the two opposite sides of the experimental ``blind-spot''. We develop alternative image modelling and analysis tools which are able to capture and recreate the ``large scale'' density patterns observed in confocal microscopy images of the nucleus. For this, we introduce a generalized Potts model which is extensively analysed also from the statistical mechanics point of view. Furthermore, we apply statistical mechanics and graph theory calculations to study patterns registered with super resolution microscopy techniques. We investigate the effect of irradiation and light stress on the structure of the chromatin, and are able to quantitatively support prior experimental observations regarding structural changes. Understanding the interaction and classification of proteins, structures which perform vastly different functions on molecular scales, is also important to achieve the final picture. We contribute to this by elaborating a framework to assess topological similarity among these chemicals. Our approach is based on recently developed computational topology algorithms used to calculate fingerprints of the molecules. We discuss three different modifications of the framework and investigate them on real-world datasets. In addition, we recognize that the mentioned fingerprints can be used to calculate the fractal dimension of certain objects, and offer an intuitive explanation for the observed relation

Humanoid Robots

For many years, the human being has been trying, in all ways, to recreate the complex mechanisms that form the human body. Such task is extremely complicated and the results are not totally satisfactory. However, with increasing technological advances based on theoretical and experimental researches, man gets, in a way, to copy or to imitate some systems of the human body. These researches not only intended to create humanoid robots, great part of them constituting autonomous systems, but also, in some way, to offer a higher knowledge of the systems that form the human body, objectifying possible applications in the technology of rehabilitation of human beings, gathering in a whole studies related not only to Robotics, but also to Biomechanics, Biomimmetics, Cybernetics, among other areas. This book presents a series of researches inspired by this ideal, carried through by various researchers worldwide, looking for to analyze and to discuss diverse subjects related to humanoid robots. The presented contributions explore aspects about robotic hands, learning, language, vision and locomotion