From a Molecule to a Drug: Chemical Features Enhancing Pharmacological Potential

This book collects contributions published in the Special Issue “From a Molecule to a Drug: Chemical Features Enhancing Pharmacological Potential” and dealing with successful stories of drug improvement or design using classic protocols, quantum mechanical mechanistic investigation, or hybrid approaches such as QM/MM or QM/ML (machine learning). In the last two decades, computer-aided modeling has strongly supported scientists’ intuition to design functional molecules. High-throughput screening protocols, mainly based on classical mechanics’ atomistic potentials, are largely employed in biology and medicinal chemistry studies with the aim of simulating drug-likeness and bioactivity in terms of efficient binding to the target receptors. The advantages of this approach are quick outcomes, the possibility of repurposing commercially available drugs, consolidated protocols, and the availability of large databases. On the other hand, these studies do not intrinsically provide reactivity information, which requires quantum mechanical methodologies that are only applicable to significantly smaller and simplified systems at present. These latter studies focus on the drug itself, considering the chemical properties related to its structural features and motifs. Overall, such simulations provide necessary insights for a better understanding of the chemistry principles that rule the diseases at the molecular level, as well as possible mechanisms for restoring the physiological equilibrium

cii Student Papers - 2021

In this collection of papers, we, the Research Group Critical Information Infrastructures (cii) from the Karlsruhe Institute of Technology, present nine selected student research articles contributing to the design, development, and evaluation of critical information infrastructures. During our courses, students mostly work in groups and deal with problems and issues related to sociotechnical challenges in the realm of (critical) information systems. Student papers came from four different cii courses, namely Emerging Trends in Digital Health, Emerging Trends in Internet Technologies, Critical Information Infrastructures, and Digital Health in the winter term of 2020 and summer term of 2021

Natural Product Genomics and Metabolomics of Marine Bacteria

Marine organisms are a treasure trove for the discovery of novel natural products, and, thus, marine natural products have been a focus of interest for researchers for decades. Some marine bacteria are prolific producers of natural products, occurring either free-living or, as recently shown, in symbiosis with marine animals. Recent advances in DNA sequencing have led to an enormous increase in published bacterial genomes and bioinformatics tools to analyze natural product biosynthetic potential by various “genome mining” approaches. Similarly, analytical NMR and MS methods for the characterization and comparison of metabolomes of natural product producers have advanced. Novel interdisciplinary approaches combine genomics and metabolomics data for accelerated and targeted natural product discovery. This Special Issue invites articles from both genomics- and metabolomics-driven studies on marine bacteria with a focus on natural product discovery and characterization. We particularly welcome articles that combine genomics and metabolomic approaches for the dereplication and characterization of marine bacterial natural products

Marine Drug Research in China: Selected Papers from the 15-NASMD Conference

The Book covers this whole field, from the discovery of structurally new and bioactive natural products (including biomacromolecules), from marine macro-/micro-organisms, to the pharmacodynamics, pharmacokinetics, metabolisms, and mechanisms of marine-derived lead compounds, both in vitro and in vivo, along with the synthesis and/or structural optimization of marine-derived lead compounds and their structure–activity relationships. Taken together, this Special Issue reprint not only provides inspiration for the discovery of marine-derived novel bioactive compounds, but also sheds light on the further research and development of marine candidate drugs

Sialotranscriptomics of the brown ear ticks, Rhipicephalus appendiculatus Neumann, 1901 and R. Zambeziensis Walker, Norval and Corwin, 1981, vectors of Corridor disease

Text in EnglishCorridor disease is an economically important tick-borne disease of cattle in southern Africa. The disease is caused by Theileria parva and transmitted by the vectors, Rhipicephalus appendiculatus and R. zambeziensis. There is currently no vaccine to protect cattle against T. parva that is permitted in South Africa. To develop recombinant anti-tick vaccines against Corridor disease, comprehensive databases of genes expressed in the tick’s salivary glands are required. Therefore, in Chapters 2 and 3, mRNA from the salivary glands of R. appendiculatus and R. zambeziensis was sequenced and assembled using next generation sequencing technologies. Respectively, 12 761 and 13 584 non-redundant protein sequences were predicted from the sialotranscriptomes of R. appendiculatus and R. zambeziensis and uploaded to public sequence domains. This greatly expanded the number of sequences available for the two vectors, which will be invaluable resources for the selection of vaccine candidates in future. Further, in Chapter 3, differential gene expression analysis in R. zambeziensis revealed dynamic expression of secretory protein transcripts during feeding, suggestive of stringent transcriptional regulation of these proteins. Knowledge of these intricate expression profiles will further assist vaccine development in future. In Chapter 4, comparative sialotranscriptomic analyses were performed between R. appendiculatus and R. zambeziensis. The ticks have previously shown varying vector competence for T. parva and this chapter presents the search for correlates of this variance. Phylogenetic analyses were performed using these and other publically available tick transcriptomes, which indicated that R. appendiculatus and R. zambeziensis are closely related but distinct species. However, significant expression differences were observed between the two ticks, specifically of genes involved in tick immunity or pathogen transmission, signifying potential bioinformatic signatures of vector competence. Furthermore, nearly four thousand putative long non-coding RNAs (lncRNAs) were predicted in each of the two ticks. A large number of these showed differential expression and suggested a potential transcriptional regulatory function of lncRNA in tick blood feeding. LncRNAs are completely unexplored in ticks. Finally, in Chapter 5, concluding remarks are given on the potential impact the R. appendiculatus and R. zambeziensis sialotranscriptomes may have on future vaccine developments and some future research endeavours are discussed.Life and Consumer SciencesPh. D. (Life Sciences

Machine Learning in Discrete Molecular Spaces

The past decade has seen an explosion of machine learning in chemistry. Whether it is in property prediction, synthesis, molecular design, or any other subdivision, machine learning seems poised to become an integral, if not a dominant, component of future research efforts. This extraordinary capacity rests on the interac- tion between machine learning models and the underlying chemical data landscape commonly referred to as chemical space. Chemical space has multiple incarnations, but is generally considered the space of all possible molecules. In this sense, it is one example of a molecular set: an arbitrary collection of molecules. This thesis is devoted to precisely these objects, and particularly how they interact with machine learning models. This work is predicated on the idea that by better understanding the relationship between molecular sets and the models trained on them we can improve models, achieve greater interpretability, and further break down the walls between data-driven and human-centric chemistry. The hope is that this enables the full predictive power of machine learning to be leveraged while continuing to build our understanding of chemistry. The first three chapters of this thesis introduce and reviews the necessary machine learning theory, particularly the tools that have been specially designed for chemical problems. This is followed by an extensive literature review in which the contributions of machine learning to multiple facets of chemistry over the last two decades are explored. Chapters 4-7 explore the research conducted throughout this PhD. Here we explore how we can meaningfully describe the properties of an arbitrary set of molecules through information theory; how we can determine the most informative data points in a set of molecules; how graph signal processing can be used to understand the relationship between the chosen molecular representation, the property, and the machine learning model; and finally how this approach can be brought to bear on protein space. Each of these sub-projects briefly explores the necessary mathematical theory before leveraging it to provide approaches that resolve the posed problems. We conclude with a summary of the contributions of this work and outline fruitful avenues for further exploration

Evaluation of the availability and applicability of computational approaches in the safety assessment of nanomaterials: Final report of the Nanocomput project

This is the final report of the Nanocomput project, the main aims of which were to review the current status of computational methods that are potentially useful for predicting the properties of engineered nanomaterials, and to assess their applicability in order to provide advice on the use of these approaches for the purposes of the REACH regulation. Since computational methods cover a broad range of models and tools, emphasis was placed on Quantitative Structure-Property Relationship (QSPR) and Quantitative Structure-Activity Relationship (QSAR) models, and their potential role in predicting NM properties. In addition, the status of a diverse array of compartment-based mathematical models was assessed. These models comprised toxicokinetic (TK), toxicodynamic (TD), in vitro and in vivo dosimetry, and environmental fate models. Finally, based on systematic reviews of the scientific literature, as well as the outputs of the EU-funded research projects, recommendations for further research and development were also made. The Nanocomput project was carried out by the European Commission’s Joint Research Centre (JRC) for the Directorate-General (DG) for Internal Market, Industry, Entrepreneurship and SMEs (DG GROW) under the terms of an Administrative Arrangement between JRC and DG GROW. The project lasted 39 months, from January 2014 to March 2017, and was supported by a steering group with representatives from DG GROW, DG Environment and the European Chemicals Agency (ECHA).JRC.F.3-Chemicals Safety and Alternative Method

Coarse-grained modeling for molecular discovery:Applications to cardiolipin-selectivity

The development of novel materials is pivotal for addressing global challenges such as achieving sustainability, technological progress, and advancements in medical technology. Traditionally, developing or designing new molecules was a resource-intensive endeavor, often reliant on serendipity. Given the vast space of chemically feasible drug-like molecules, estimated between 106 - 10100 compounds, traditional in vitro techniques fall short.Consequently, in silico tools such as virtual screening and molecular modeling have gained increasing recognition. However, the computational cost and the limited precision of the utilized molecular models still limit computational molecular design.This thesis aimed to enhance the molecular design process by integrating multiscale modeling and free energy calculations. Employing a coarse-grained model allowed us to efficiently traverse a significant portion of chemical space and reduce the sampling time required by molecular dynamics simulations. The physics-informed nature of the applied Martini force field and its level of retained structural detail make the model a suitable starting point for the focused learning of molecular properties.We applied our proposed approach to a cardiolipin bilayer, posing a relevant and challenging problem and facilitating reasonable comparison to experimental measurements.We identified promising molecules with defined properties within the resolution limit of a coarse-grained representation. Furthermore, we were able to bridge the gap from in silico predictions to in vitro and in vivo experiments, supporting the validity of the theoretical concept. The findings underscore the potential of multiscale modeling and free-energy calculations in enhancing molecular discovery and design and offer a promising direction for future research