Recommender systems in antiviral drug discovery

Recommender systems (RSs), which underwent rapid development and had an enormous impact on e-commerce, have the potential to become useful tools for drug discovery. In this paper, we applied RS methods for the prediction of the antiviral activity class (active/inactive) for compounds extracted from ChEMBL. Two main RS approaches were applied: Collaborative filtering (Surprise implementation) and content-based filtering (sparse-group inductive matrix completion (SGIMC) method). The effectiveness of RS approaches was investigated for prediction of antiviral activity classes ("interactions") for compounds and viruses, for which some of their interactions with other viruses or compounds are known, and for prediction of interaction profiles for new compounds. Both approaches achieved relatively good prediction quality for binary classification of individual interactions and compound profiles, as quantified by cross-validation and external validation receiver operating characteristic (ROC) score >0.9. Thus, even simple recommender systems may serve as an effective tool in antiviral drug discovery

Chemical Information Bulletin

Periodic supplement for "the regular journals of the American Chemical Society," containing annotated bibliographies of chemical documentation literature as well as information about meetings, conferences, awards, scholarships, and other news from the American Chemical Society (ACS) Division of Chemical Literature

Cheminformatics Tools to Explore the Chemical Space of Peptides and Natural Products

Cheminformatics facilitates the analysis, storage, and collection of large quantities of chemical data, such as molecular structures and molecules' properties and biological activity, and it has revolutionized medicinal chemistry for small molecules. However, its application to larger molecules is still underrepresented. This thesis work attempts to fill this gap and extend the cheminformatics approach towards large molecules and peptides. This thesis is divided into two parts. The first part presents the implementation and application of two new molecular descriptors: macromolecule extended atom pair fingerprint (MXFP) and MinHashed atom pair fingerprint of radius 2 (MAP4). MXFP is an atom pair fingerprint suitable for large molecules, and here, it is used to explore the chemical space of non-Lipinski molecules within the widely used PubChem and ChEMBL databases. MAP4 is a MinHashed hybrid of substructure and atom pair fingerprints suitable for encoding small and large molecules. MAP4 is first benchmarked against commonly used atom pairs and substructure fingerprints, and then it is used to investigate the chemical space of microbial and plants natural products with the aid of machine learning and chemical space mapping. The second part of the thesis focuses on peptides, and it is introduced by a review chapter on approaches to discover novel peptide structures and describing the known peptide chemical space. Then, a genetic algorithm that uses MXFP in its fitness function is described and challenged to generate peptide analogs of peptidic or non-peptidic queries. Finally, supervised and unsupervised machine learning is used to generate novel antimicrobial and non-hemolytic peptide sequences

Semantic distances between medical entities

In this thesis, three different similarity measures between medical entities (drugs) have been implemented. Each of those measures have been computed over one or more dimensions of the drugs: textual, taxonomic and molecular information. All the information has been extracted from the same resource, the DrugBank database

The essentials of marine biotechnology.

Coastal countries have traditionally relied on the existing marine resources (e.g., fishing, food, transport, recreation, and tourism) as well as tried to support new economic endeavors (ocean energy, desalination for water supply, and seabed mining). Modern societies and lifestyle resulted in an increased demand for dietary diversity, better health and well-being, new biomedicines, natural cosmeceuticals, environmental conservation, and sustainable energy sources. These societal needs stimulated the interest of researchers on the diverse and underexplored marine environments as promising and sustainable sources of biomolecules and biomass, and they are addressed by the emerging field of marine (blue) biotechnology. Blue biotechnology provides opportunities for a wide range of initiatives of commercial interest for the pharmaceutical, biomedical, cosmetic, nutraceutical, food, feed, agricultural, and related industries. This article synthesizes the essence, opportunities, responsibilities, and challenges encountered in marine biotechnology and outlines the attainment and valorization of directly derived or bio-inspired products from marine organisms. First, the concept of bioeconomy is introduced. Then, the diversity of marine bioresources including an overview of the most prominent marine organisms and their potential for biotechnological uses are described. This is followed by introducing methodologies for exploration of these resources and the main use case scenarios in energy, food and feed, agronomy, bioremediation and climate change, cosmeceuticals, bio-inspired materials, healthcare, and well-being sectors. The key aspects in the fields of legislation and funding are provided, with the emphasis on the importance of communication and stakeholder engagement at all levels of biotechnology development. Finally, vital overarching concepts, such as the quadruple helix and Responsible Research and Innovation principle are highlighted as important to follow within the marine biotechnology field. The authors of this review are collaborating under the European Commission-funded Cooperation in Science and Technology (COST) Action Ocean4Biotech – European transdisciplinary networking platform for marine biotechnology and focus the study on the European state of affairs

TOWARDS UNDERSTANDING MODE-OF-ACTION OF TRADITIONAL MEDICINES BY USING IN SILICO TARGET PREDICTION

Traditional medicines (TM) have been used for centuries to treat illnesses, but in many cases their modes-of-action (MOAs) remain unclear. Given the increasing data of chemical ingredients of traditional medicines and the availability of large-scale bioactivity data linking chemical structures to activities against protein targets, we are now in a position to propose computational hypotheses for the MOAs using in silico target prediction. The MOAs were established from supporting literature. The in silico target prediction, which is based on the “Molecular Similarity Principle”, was modelled via two models: a Naïve Bayes Classifier and a Random Forest Classifier. Chapter 2 discovered the relationship of 46 traditional Chinese medicine (TCM) therapeutic action subclasses by mapping them into a dendrogram using the predicted targets. Overall, the most frequent top three enriched targets/pathways were immune-related targets such as tyrosine-protein phosphatase non-receptor type 2 (PTPN2) and digestive system such as mineral absorption. Two major protein families, G-protein coupled receptor (GPCR), and protein kinase family contributed to the diversity of the bioactivity space, while digestive system was consistently annotated pathway motif. Chapter 3 compared the chemical and bioactivity space of 97 anti-cancer plants’ compounds of TCM, Ayurveda and Malay traditional medicine. The comparison of the chemical space revealed that benzene, anthraquinone, flavone, sterol, pentacyclic triterpene and cyclohexene were the most frequent scaffolds in those TM. The annotation of the bioactivity space with target classes showed that kinase class was the most significant target class for all groups. From a phylogenetic tree of the anti-cancer plants, only eight pairs of plants were phylogenetically related at either genus, family or order level. Chapter 4 evaluated synergy score of pairwise compound combination of Shexiang Baoxin Pill (SBP), a TCM formulation for myocardial infarction. The score was measured from the topological properties, pathway dissimilarity and mean distance of all the predicted targets of a combination on a representative network of the disease. The method found four synergistic combinations, ginsenoside Rb3 and cholic acid, ginsenoside Rb2 and ginsenoside Rb3, ginsenoside Rb3 and 11-hydroxyprogesterone and ginsenoside Rb2 and ginsenoside Rd agreed with the experimental results. The modulation of androgen receptor, epidermal growth factor and caspases were proposed for the synergistic actions. Altogether, in silico target prediction was able to discover the bioactivity space of different TMs and elucidate the MOA of multiple formulations and two major health concerns: cancer and myocardial infarction. Hence, understanding the MOA of the traditional medicine could be beneficial in providing testable hypotheses to guide towards finding new molecular entities

Modelling the genomic structure, and antiviral susceptibility of Human Cytomegalovirus

Human Cytomegalovirus (HCMV) is found ubiquitously in humans worldwide, and once acquired, the infection persists within the host throughout their life. Although Immunocompetent people rarely are affected by HCMV infections, their related diseases pose a major health problem worldwide for those with compromised or suppressed immune systems such as transplant recipients. Additionally, congenital transmission of HCMV is the most common infectious cause of birth defects globally and is associated with a substantial economic burden. This thesis explores the application of statistical modelling and genomics to unpick three key areas of interest in HCMV research. First, a comparative genomics analysis of global HCMV strains was undertaken to delineate the molecular population structure of this highly variable virus. By including in-house sequenced viruses of African origin and by developing a statistical framework to deconvolute highly variable regions of the genome, novel and important insights into the co-evolution of HCMV with its host were uncovered. Second, a rich database relating mutations to drug sensitivity was curated for all the antiviral treated herpesviruses. This structured information along with the development of a mutation annotation pipeline, allowed the further development of statistical models that predict the phenotype of a virus from its sequence. The predictive power of these models was validated for HSV1 by using external unseen mutation data provided in collaboration with the UK Health Security Agency. Finally, a nonlinear mixed effects model, expanded to account for Ganciclovir pharmacokinetics and pharmacodynamics, was developed by making use of rich temporal HCMV viral load data. This model allowed the estimation of the impact of immune-clearance versus antiviral inhibition in controlling HCMV lytic replication in already established infections post-haematopoietic stem cell transplant

Chemical Information Bulletin

Created as a supplement for "the regular journals of the American Chemical Society," this publication contains annotated bibliographies of chemical documentation literature as well as information about meetings, conferences, awards, scholarships, and other news from the American Chemical Society (ACS) Division of Chemical Information (CINF)

In silico investigation of hepatitis c virus: a novel perspective into targeted viral inhibition of NS3 helicase, NS 3/4a protease and NS5b RNA dependent RNA polymerase.

Doctoral Degrees (Pharmaceutical Sciences). University of KwaZulu-Natal. Westville, 2019.Hepatitis C Virus (HCV) is an escalating global healthcare and economic burden that requires extensive intervention to alleviate its control. Over the years, drug design efforts have produced many anti-HCV drugs; however, due to drug resistance brought on by numerous genetic variations of the virus and lack of specificity and stability, current drugs are rendered ineffective. The situation has been further intensified by the absence of a viable vaccine. For these reasons, continuous HCV research is imperative for the design and development of promising inhibitors that address the challenges faced by present antiviral therapies. Moreover, exposure of previously neglected viral protein targets can offer another potentially valuable therapeutic route in drug design research. Structure-based drug design approaches accentuate the development of small inhibitor molecules that interact with therapeutic targets through non-covalent interactions. The unexpected discovery of covalent inhibitors and their distinctive nature of instigating complete and irreversible inhibition of targets have shifted attention away from the use of non-covalent drugs in antiviral treatment. This has led to significant progress in understanding covalent inhibition regarding their underlying mechanism of action and in the design of novel covalent inhibitors that work against biological targets. However, due to difficulties arising in its application and resultant safety, the pharmaceutical industry were reluctant to pursue this strategy. With the use of rational drug design, a novel strategy was then proposed known as selective covalent inhibition. Due to the lack of competent protocols and information, little is known regarding selective covalent inhibition This study investigates three biological HCV targets, NS3 protease, RNA helicase and NS5B RNAdependent RNA polymerase. With constantly evolving viruses like HCV, computational methods including molecular modelling and docking, virtual screening and molecular dynamic simulations have allowed chemists to screen millions of compounds to filter out potential lead drugs. These in silico approaches have allowed Computer-Aided Drug Design as a cost-effective strategy to accelerate the process of drug discovery. The above techniques, with numerous other computational tools were employed in this study to fill the gap in HCV drug research by providing insights into the structural and dynamic changes that describe the mechanism of selective covalent inhibition and pharmacophoric features that lead to unearthing of potential small inhibitor molecules against Hepatitis C. v The first study (Chapter 4) provides a comprehensive review on HCV NS3/4A protein, current therapies and covalent inhibition as well as introduces a technical guideline that provides a systematic approach for the design and development of potent, selective HCV inhibitors. The second study (Chapter 5) provides a comprehensive understanding concerning the implications of selective covalent inhibition on the activity of HCV NS5B RNA-dependent RNA polymerase, with respect to key components required for viral replication, when bound to a target-specific small inhibitor molecule. The third study (Chapter 6) is preliminary investigation that uses Pharmacophore-based virtual screening as an efficient tool for the discovery of improved potential HCV NS3 helicase inhibitors. The pharmacophoric features were created based on the highly contributing amino acid residues that bind with highest affinity to the weak inhibitor, quercetin. These residues were identified based on free energy footprints obtained from molecular dynamic and thermodynamic calculations. Post molecular dynamic analysis and appropriate drug-likeness properties of the three top-hit compounds revealed that ZINC02495613 could be a more effective potential HCV helicase inhibitor; however, further validation steps are still required. This study offers a comprehensive in silico perspective to fill the gap in rational drug design research against HCV, thus providing an insight into the mechanism of selective covalent inhibition, uncovering a previously neglected viral target and identifying possible antiviral drugs. To this end, the work presented in this report is considered a fundamental platform to advance research toward the design and development of novel and selective anti-HCV drugs