Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

A Computational Framework for Host-Pathogen Protein-Protein Interactions

Infectious diseases cause millions of illnesses and deaths every year, and raise great health concerns world widely. How to monitor and cure the infectious diseases has become a prevalent and intractable problem. Since the host-pathogen interactions are considered as the key infection processes at the molecular level for infectious diseases, there have been a large amount of researches focusing on the host-pathogen interactions towards the understanding of infection mechanisms and the development of novel therapeutic solutions. For years, the continuously development of technologies in biology has benefitted the wet lab-based experiments, such as small-scale biochemical, biophysical and genetic experiments and large-scale methods (for example yeast-two-hybrid analysis and cryogenic electron microscopy approach). As a result of past decades of efforts, there has been an exploded accumulation of biological data, which includes multi omics data, for example, the genomics data and proteomics data. Thus, an initiative review of omics data has been conducted in Chapter 2, which has exclusively demonstrated the recent update of ‘omics’ study, particularly focusing on proteomics and genomics. With the high-throughput technologies, the increasing amount of ‘omics’ data, including genomics and proteomics, has even further boosted. An upsurge of interest for data analytics in bioinformatics comes as no surprise to the researchers from a variety of disciplines. Specifically, the astonishing rate at which genomics and proteomics data are generated leads the researchers into the realm of ‘Big Data’ research. Chapter 2 is thus developed to providing an update of the omics background and the state-of-the-art developments in the omics area, with a focus on genomics data, from the perspective of big data analytics..

Structure-activity approaches for prediction of chemical reactivity and pharmacological properties of some heterocyclic compounds

Benzodiazepine drugs are widely prescribed to treat many psychiatric and neurologic disorders. As its pharmacological action is exerted in a sensitive area of the brain; ''the central nervous system'', it is crucial to provide detailed reports on the chemistry of benzodiazepines, model the mechanism of action that occurs with GABAA receptors, identify the overlap with other modulators, as well as explore the structural requirements that better potentiate the receptor response to benzodiazepines. This dissertation consists of two original studies that consider the new lines of research related to benzodiazepines, particularly the identification of three new TMD binding sites. The first study provides, on the one hand, an overview of the chemistry of six Benzodiazepine basic rings starting from structural characteristics, electronic properties, Global/local reactivities, up to intermolecular interactions with long-range nucleophilic/electrophilic reactants. This was achieved by combining a DFT investigation with a quantitative MEP analysis on the vdW surface. On the other hand, the performed molecular docking simulations allowed identifying the best binding modes, binding interactions, and binding affinities with residues, which helped to validate the quantitative MEP analysis predictions. The second study was conducted on a dataset of [3H]diazepam derivatives. First, molecular docking simulation was used to initially screen the dataset and select the best ligand/target complexes. Afterwise, the best-docked complexes were refined by performing molecular dynamics simulation for 1000 picoseconds. For both simulations, the binding modes, binding interactions, and binding affinities were thoroughly discussed and compared with each other and with outcomes collected from the literature. Additionally, the good pharmacokinetic properties (ADME prediction) as well as compliance with all druglikeness rules were checked via in silico tools for all the dataset compounds. Finally, a QSAR analysis was carried out using an improved version of PLS regression. Briefly, the dataset is randomly split into 10 000 training and test sets that involve, respectively, 80% and 20% of chemicals. Subsequently, 10 000 statistical simulations were conducted that; after excluding outlying observations, yielded 10 000 best training models following the Bayesian Information Criterion. Among these 10 000 best models, the best predictors with the highest probability of occurrence were selected. As a consequence, the derived PLS regression equation explains 63.2% of the variability in BDZ activity around its mean. Furthermore, Internal and external validation metrics assure the robustness and predictability of the developed model. The developed model was interpreted based on literature investigations and a combination of implemented approaches