Computational Approaches to Drug Profiling and Drug-Protein Interactions

Despite substantial increases in R&D spending within the pharmaceutical industry, denovo drug design has become a time-consuming endeavour. High attrition rates led to a long period of stagnation in drug approvals. Due to the extreme costs associated with introducing a drug to the market, locating and understanding the reasons for clinical failure is key to future productivity. As part of this PhD, three main contributions were made in this respect. First, the web platform, LigNFam enables users to interactively explore similarity relationships between ‘drug like’ molecules and the proteins they bind. Secondly, two deep-learning-based binding site comparison tools were developed, competing with the state-of-the-art over benchmark datasets. The models have the ability to predict offtarget interactions and potential candidates for target-based drug repurposing. Finally, the open-source ScaffoldGraph software was presented for the analysis of hierarchical scaffold relationships and has already been used in multiple projects, including integration into a virtual screening pipeline to increase the tractability of ultra-large screening experiments. Together, and with existing tools, the contributions made will aid in the understanding of drug-protein relationships, particularly in the fields of off-target prediction and drug repurposing, helping to design better drugs faster

The prospects of quantum computing in computational molecular biology

Quantum computers can in principle solve certain problems exponentially more quickly than their classical counterparts. We have not yet reached the advent of useful quantum computation, but when we do, it will affect nearly all scientific disciplines. In this review, we examine how current quantum algorithms could revolutionize computational biology and bioinformatics. There are potential benefits across the entire field, from the ability to process vast amounts of information and run machine learning algorithms far more efficiently, to algorithms for quantum simulation that are poised to improve computational calculations in drug discovery, to quantum algorithms for optimization that may advance fields from protein structure prediction to network analysis. However, these exciting prospects are susceptible to "hype", and it is also important to recognize the caveats and challenges in this new technology. Our aim is to introduce the promise and limitations of emerging quantum computing technologies in the areas of computational molecular biology and bioinformatics.Comment: 23 pages, 3 figure

Clustering Algorithms for Microarray Data Mining

This thesis presents a systems engineering model of modern drug discovery processes and related systems integration requirements. Some challenging problems include the integration of public information content with proprietary corporate content, supporting different types of scientific analyses, and automated analysis tools motivated by diverse forms of biological data.To capture the requirements of the discovery system, we identify the processes, users, and scenarios to form a UML use case model. We then define the object-oriented system structure and attach behavioral elements. We also look at how object-relational database extensions can be applied for such analysis.The next portion of the thesis studies the performance of clustering algorithms based on LVQ, SVMs, and other machine learning algorithms, to two types of analyses - functional and phenotypic classification. We found that LVQ initialized with the LBG codebook yields comparable performance to the optimal separating surfaces generated by related SVM kernels. We also describe a novel similarity measure, called the unnormalized symmetric Kullback-Liebler measure, based on unnormalized expression values. Since the Mercer criterion cannot be applied to this measure, we compared the performance of this similarity measure with the log-Euclidean distance in the LVQ algorithm.The two distance measures perform similarly on cDNA arrays, while the unnormalized symmetric Kullback-Liebler measure outperforms the log-Euclidean distance on certain phenotypic classification problems. Pre-filtering algorithms to find discriminating instances based on PCA, the Find Similar function, and IB3 were also investigated. The Find Similar method gives the best performance in terms of multiple criteria