CIME4R: Exploring iterative, AI-guided chemical reaction optimization campaigns in their parameter space.

Chemical reaction optimization (RO) is an iterative process that results in large, high-dimensional datasets. Current tools allow for only limited analysis and understanding of parameter spaces, making it hard for scientists to review or follow changes throughout the process. With the recent emergence of using artificial intelligence (AI) models to aid RO, another level of complexity has been added. Helping to assess the quality of a model's prediction and understand its decision is critical to supporting human-AI collaboration and trust calibration. To address this, we propose CIME4R-an open-source interactive web application for analyzing RO data and AI predictions. CIME4R supports users in (i) comprehending a reaction parameter space, (ii) investigating how an RO process developed over iterations, (iii) identifying critical factors of a reaction, and (iv) understanding model predictions. This facilitates making informed decisions during the RO process and helps users to review a completed RO process, especially in AI-guided RO. CIME4R aids decision-making through the interaction between humans and AI by combining the strengths of expert experience and high computational precision. We developed and tested CIME4R with domain experts and verified its usefulness in three case studies. Using CIME4R the experts were able to produce valuable insights from past RO campaigns and to make informed decisions on which experiments to perform next. We believe that CIME4R is the beginning of an open-source community project with the potential to improve the workflow of scientists working in the reaction optimization domain. SCIENTIFIC CONTRIBUTION: To the best of our knowledge, CIME4R is the first open-source interactive web application tailored to the peculiar analysis requirements of reaction optimization (RO) campaigns. Due to the growing use of AI in RO, we developed CIME4R with a special focus on facilitating human-AI collaboration and understanding of AI models. We developed and evaluated CIME4R in collaboration with domain experts to verify its practical usefulness.

Functionalization of Ti64 via Direct Laser Interference Patterning and Its Influence on Wettability and Oxygen Bubble Nucleation.

The nucleation of bubbles on solid surfaces is an important phenomenon in nature and technological processes like electrolysis. During proton-exchange membrane electrolysis, the nucleation and separation of the electrically nonconductive oxygen in the anodic cycle plays a crucial role to minimize the overpotential it causes in the system. This increases the efficiency of the process, making renewable energy sources and the "power-to-gas" strategy more viable. A promising approach is to optimize gas separation by surface functionalization in order to apply a more advantageous interface to industrial materials. In this work, the connection between the wettability and bubble nucleation of oxygen is investigated. For tailoring the wettability of Ti64 substrates, the direct laser interference patterning method is applied. A laser source with a wavelength of 1064 nm and a pulse duration of 12 ps is used to generate periodic pillar-like structures with different depths up to ∼5 µm. The resulting surface properties are characterized by water contact angle measurement, scanning electron microscopy, confocal microscopy, and X-ray photon spectroscopy. It was possible to generate structures with a water contact angle ranging from 20° up to nearly superhydrophobic conditions. The different wettabilities are validated based on X-ray photon spectroscopy and the different elemental composition of the samples. The results indicate that the surface character of the substrate adapts depending on the surrounding media and needs more time to reach a steady state for deeper structures. A custom setup is used to expose the functionalized surfaces to oxygen-oversaturated solutions. It is shown that a higher hydrophobicity of the structured surface yields a stronger interaction with the dissolved gas. This significantly enhances the oxygen nucleation up to nearly 350% by generating approximately 20 times more nucleation spots, but also smaller bubble sizes and a reduced detachment rate.

Gold(III) complexes with thiosemicarbazone ligands: insights into their cytotoxic effects on lung cancer cells.

Cancer continues to pose a global threat, underscoring the urgent need for more effective and safer treatment options. Gold-based compounds have recently emerged as promising candidates due to their diverse range of biological activities. In this study, three gold(III) complexes derived from thiosemicarbazone ligands have been synthesized, fully characterized, including their X-ray crystal structures. We conducted initial mode-of-action studies on DNA and BSA, followed by a comprehensive investigation into the cytotoxic effects of these novel gold(III) complexes on lung cancer cells (A549, H2052, and H28). The results demonstrated a concentration-dependent cytotoxic response, with H28 cells exhibiting the highest sensitivity to the treatment. Furthermore, the analysis of the cell cycle revealed that these compounds induce cell cycle arrest and promote apoptosis as a response to treatment. We also observed distinct morphological changes and increased oxidative stress, contributing significantly to cell death. Notably, these complexes exhibited the ability to suppress interleukin-6 production in mesothelioma cell lines, and this highlights their anti-inflammatory potential. To gain an initial understanding of cytotoxicity on healthy cells, hemolysis tests were conducted against human blood cells, with no evidence of hemolysis. Furthermore, a toxicity assessment through the in vivo Galleria mellonella model underscored the absence of detectable toxicity. These findings prove that these complexes are promising novel therapeutic agents for lung cancer.

Impact of N-heteroaromatic N-termini in Cu(II) ATCUN metallopeptides on their biorelevant redox activity.

Cu(II) complexes with ATCUN peptide ligands have been investigated for their ROS (reactive oxygen species) generation and oxidative DNA degradation abilities. The biological activity of most ATCUN complexes such as Cu-GGH (Gly-Gly-His) is, however, low. Tuning the redox chemistry by incorporation of N-heteroaromatics reinstates ROS production which leads to efficient DNA cleavage.

XSMILES: interactive visualization for molecules, SMILES and XAI attribution scores.

BACKGROUND: Explainable artificial intelligence (XAI) methods have shown increasing applicability in chemistry. In this context, visualization techniques can highlight regions of a molecule to reveal their influence over a predicted property. For this purpose, some XAI techniques calculate attribution scores associated with tokens of SMILES strings or with atoms of a molecule. While an association of a score with an atom can be directly visually represented on a molecule diagram, scores computed for SMILES non-atom tokens cannot. For instance, a substring [N+] contains 3 non-atom tokens, i.e., [, [Formula: see text], and ], and their attributions, depending on the model, are not necessarily revealing an influence of the nitrogen atom over the predicted property; for that reason, it is not possible to represent the scores on a molecule diagram. Moreover, SMILES's notation is complex, foregrounding the need for techniques to facilitate the analysis of explanations associated with their tokens. RESULTS: We propose XSMILES, an interactive visualization technique, to explore explainable artificial intelligence attributions scores and support the interpretation of SMILES. Users can input any type of score attributed to atom and non-atom tokens and visualize them on top of a 2D molecule diagram coordinated with a bar chart that represents a SMILES string. We demonstrate how attributions calculated for SMILES strings can be evaluated and better interpreted through interactivity with two use cases. CONCLUSIONS: Data scientists can use XSMILES to understand their models' behavior and compare multiple modeling approaches. The tool provides a set of parameters to adapt the visualization to users' needs and it can be integrated into different platforms. We believe XSMILES can support data scientists to develop, improve, and communicate their models by making it easier to identify patterns and compare attributions through interactive exploratory visualization.

ChemInformatics Model Explorer (CIME): exploratory analysis of chemical model explanations.

The introduction of machine learning to small molecule research- an inherently multidisciplinary field in which chemists and data scientists combine their expertise and collaborate - has been vital to making screening processes more efficient. In recent years, numerous models that predict pharmacokinetic properties or bioactivity have been published, and these are used on a daily basis by chemists to make decisions and prioritize ideas. The emerging field of explainable artificial intelligence is opening up new possibilities for understanding the reasoning that underlies a model. In small molecule research, this means relating contributions of substructures of compounds to their predicted properties, which in turn also allows the areas of the compounds that have the greatest influence on the outcome to be identified. However, there is no interactive visualization tool that facilitates such interdisciplinary collaborations towards interpretability of machine learning models for small molecules. To fill this gap, we present CIME (ChemInformatics Model Explorer), an interactive web-based system that allows users to inspect chemical data sets, visualize model explanations, compare interpretability techniques, and explore subgroups of compounds. The tool is model-agnostic and can be run on a server or a workstation.

Collaborative Sense-Making in Genomic Research: The Role of Visualisation.

Genomic research emerges from collaborative work within and across different scientific disciplines. A diverse range of visualisation techniques has been employed to aid this research, yet relatively little is known as to how these techniques facilitate collaboration. We conducted a case study of collaborative research within a biomedical institute to learn more about the role visualisation plays in genomic mapping. Interviews were conducted with molecular biologists (N = 5) and bioinformaticians (N = 6). We found that genomic research comprises a variety of distinct disciplines engaged in complex analytic tasks that each resist simplification, and their complexity influences how visualisations were used. Visualisation use was impacted by group-specific interactions and temporal work patterns. Visualisations were also crucial to the scientific workflow, used for both question formation and confirmation of hypotheses, and acted as an anchor for the communication of ideas and discussion. In the latter case, two approaches were taken: providing collaborators with either interactive or static imagery representing a viewpoint. The use of generic software for simplified visualisations, and quick production and curation was also noted. We discuss these findings with reference to group-specific interactions and present recommendations for improving collaborative practices through visual analytics.

Incorporation of ß-Alanine in Cu(II) ATCUN Peptide Complexes Increases ROS Levels, DNA Cleavage and Antiproliferative Activity.

Redox-active Cu(II) complexes are able to form reactive oxygen species (ROS) in the presence of oxygen and reducing agents. Recently, Faller et al. reported that ROS generation by Cu(II) ATCUN complexes is not as high as assumed for decades. High complex stability results in silencing of the Cu(II)/Cu(I) redox cycle and therefore leads to low ROS generation. In this work, we demonstrate that an exchange of the α-amino acid Gly with the ß-amino acid ß-Ala at position 2 (Gly2→ß-Ala2) of the ATCUN motif reinstates ROS production (• OH and H2 O2 ). Potentiometry, cyclic voltammetry, EPR spectroscopy and DFT simulations were utilized to explain the increased ROS generation of these ß-Ala2-containing ATCUN complexes. We also observed enhanced oxidative cleavage activity towards plasmid DNA for ß-Ala2 compared to the Gly2 complexes. Modifications with positively charged Lys residues increased the DNA affinity through electrostatic interactions as determined by UV/VIS, fluorescence, and CD spectroscopy, and consequently led to a further increase in nuclease activity. A similar trend was observed regarding the cytotoxic activity of the complexes against several human cancer cell lines where ß-Ala2 peptide complexes had lower IC50 values compared to Gly2. The higher cytotoxicity could be attributed to an increased cellular uptake as determined by ICP-MS measurements.

Forty Years after the Discovery of Its Nucleolytic Activity: [Cu(phen)2 ]2+ Shows Unattended DNA Cleavage Activity upon Fluorination.

[Cu(phen)2 ]2+ (phen=1,10-phenanthroline) is the first and still one of the most efficient artificial nucleases. In general, when the phen ligand is modified, the nucleolytic activity of its CuII complex is significantly reduced. This is most likely due to higher steric bulk of such ligands and thus lower affinity to DNA. CuII complexes with phen ligands having fluorinated substituents (F, CF3 , SF5 , SCF3 ) surprisingly showed excellent DNA cleavage activity-in contrast to the unsubstituted [Cu(phen)2 ]2+ -in the absence of the otherwise required classical, bioabundant external reducing agents like thiols or ascorbate. This nucleolytic activity correlates well with the half-wave potentials E1/2 of the complexes. Cancer cell studies show cytotoxic effects of all complexes with fluorinated ligands in the low µm range, whereas they were less toxic towards healthy cells (fibroblasts).

Visualizing metabolic network dynamics through time-series metabolomic data.

BACKGROUND: New technologies have given rise to an abundance of -omics data, particularly metabolomic data. The scale of these data introduces new challenges for the interpretation and extraction of knowledge, requiring the development of innovative computational visualization methodologies. Here, we present GEM-Vis, an original method for the visualization of time-course metabolomic data within the context of metabolic network maps. We demonstrate the utility of the GEM-Vis method by examining previously published data for two cellular systems-the human platelet and erythrocyte under cold storage for use in transfusion medicine. RESULTS: The results comprise two animated videos that allow for new insights into the metabolic state of both cell types. In the case study of the platelet metabolome during storage, the new visualization technique elucidates a nicotinamide accumulation that mirrors that of hypoxanthine and might, therefore, reflect similar pathway usage. This visual analysis provides a possible explanation for why the salvage reactions in purine metabolism exhibit lower activity during the first few days of the storage period. The second case study displays drastic changes in specific erythrocyte metabolite pools at different times during storage at different temperatures. CONCLUSIONS: The new visualization technique GEM-Vis introduced in this article constitutes a well-suitable approach for large-scale network exploration and advances hypothesis generation. This method can be applied to any system with data and a metabolic map to promote visualization and understand physiology at the network level. More broadly, we hope that our approach will provide the blueprints for new visualizations of other longitudinal -omics data types. The supplement includes a comprehensive user's guide and links to a series of tutorial videos that explain how to prepare model and data files, and how to use the software SBMLsimulator in combination with further tools to create similar animations as highlighted in the case studies.

ClinVAP: a reporting strategy from variants to therapeutic options.

MOTIVATION: Next-generation sequencing has become routine in oncology and opens up new avenues of therapies, particularly in personalized oncology setting. An increasing number of cases also implies a need for a more robust, automated and reproducible processing of long lists of variants for cancer diagnosis and therapy. While solutions for the large-scale analysis of somatic variants have been implemented, existing solutions often have issues with reproducibility, scalability and interoperability. RESULTS: Clinical Variant Annotation Pipeline (ClinVAP) is an automated pipeline which annotates, filters and prioritizes somatic single nucleotide variants provided in variant call format. It augments the variant information with documented or predicted clinical effect. These annotated variants are prioritized based on driver gene status and druggability. ClinVAP is available as a fully containerized, self-contained pipeline maximizing reproducibility and scalability allowing the analysis of larger scale data. The resulting JSON-based report is suited for automated downstream processing, but ClinVAP can also automatically render the information into a user-defined template to yield a human-readable report. AVAILABILITY AND IMPLEMENTATION: ClinVAP is available at https://github.com/PersonalizedOncology/ClinVAP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Flexible vs. rigid bis(2-benzimidazolyl) ligands in Cu(II) complexes: Impact on redox chemistry and oxidative DNA cleavage activity.

Cu(II) complexes of bis(2-benzimidazolyl) ligands connected by different linker moieties (disulfide, ethylene, ortho-phenylene) were applied in DNA cleavage reactions. Hydroxyl radicals and hydrogen peroxide were proven as reactive oxygen species (ROS) in a DNA quenching experiment. Thus, an oxidative DNA cleavage mechanism is suggested. The binding affinity of the Cu(II) complexes to DNA was studied by UV-VIS (DNA melting), fluorescence (ethidium bromide displacement assay) and circular dichroism (CD) spectroscopy indicating a correlation between DNA binding and DNA cleavage efficiency. The most important finding was that oxidative nuclease activity correlated with flexibility of the linker between the benzimidazole moieties. A more flexible linker allowed for an easier switch between square planar (Cu(II)) and tetrahedral geometry (Cu(I)) for the complex, and thus resulted in an enhanced ROS generation. EPR spectroscopy and cyclic voltammetry were applied to investigate such changes in geometry and redox state.

Unexpected features of the dark proteome.

We surveyed the "dark" proteome-that is, regions of proteins never observed by experimental structure determination and inaccessible to homology modeling. For 546,000 Swiss-Prot proteins, we found that 44-54% of the proteome in eukaryotes and viruses was dark, compared with only ∼14% in archaea and bacteria. Surprisingly, most of the dark proteome could not be accounted for by conventional explanations, such as intrinsic disorder or transmembrane regions. Nearly half of the dark proteome comprised dark proteins, in which the entire sequence lacked similarity to any known structure. Dark proteins fulfill a wide variety of functions, but a subset showed distinct and largely unexpected features, such as association with secretion, specific tissues, the endoplasmic reticulum, disulfide bonding, and proteolytic cleavage. Dark proteins also had short sequence length, low evolutionary reuse, and few known interactions with other proteins. These results suggest new research directions in structural and computational biology.

Integrated visual analysis of protein structures, sequences, and feature data.

BACKGROUND: To understand the molecular mechanisms that give rise to a protein's function, biologists often need to (i) find and access all related atomic-resolution 3D structures, and (ii) map sequence-based features (e.g., domains, single-nucleotide polymorphisms, post-translational modifications) onto these structures. RESULTS: To streamline these processes we recently developed Aquaria, a resource offering unprecedented access to protein structure information based on an all-against-all comparison of SwissProt and PDB sequences. In this work, we provide a requirements analysis for several frequently occuring tasks in molecular biology and describe how design choices in Aquaria meet these requirements. Finally, we show how the interface can be used to explore features of a protein and gain biologically meaningful insights in two case studies conducted by domain experts. CONCLUSIONS: The user interface design of Aquaria enables biologists to gain unprecedented access to molecular structures and simplifies the generation of insight. The tasks involved in mapping sequence features onto structures can be conducted easier and faster using Aquaria.

Visualising intrinsic disorder and conformational variation in protein ensembles.

Intrinsically disordered regions (IDRs) in proteins are still not well understood, but are increasingly recognised as important in key biological functions, as well as in diseases. IDRs often confound experimental structure determination-however, they are present in many of the available 3D structures, where they exhibit a wide range of conformations, from ill-defined and highly flexible to well-defined upon binding to partner molecules, or upon post-translational modifications. Analysing such large conformational variations across ensembles of 3D structures can be complex and difficult; our goal in this paper is to improve this situation by augmenting traditional approaches (molecular graphics and principal components) with methods from human-computer interaction and information visualisation, especially parallel coordinates. We present a new tool integrating these approaches, and demonstrate how it can dissect ensembles to reveal functional insights into conformational variation and intrinsic disorder.

A visual analytics approach for models of heterogeneous cell populations.

In recent years, cell population models have become increasingly common. In contrast to classic single cell models, population models allow for the study of cell-to-cell variability, a crucial phenomenon in most populations of primary cells, cancer cells, and stem cells. Unfortunately, tools for in-depth analysis of population models are still missing. This problem originates from the complexity of population models. Particularly important are methods to determine the source of heterogeneity (e.g., genetics or epigenetic differences) and to select potential (bio-)markers. We propose an analysis based on visual analytics to tackle this problem. Our approach combines parallel-coordinates plots, used for a visual assessment of the high-dimensional dependencies, and nonlinear support vector machines, for the quantification of effects. The method can be employed to study qualitative and quantitative differences among cells. To illustrate the different components, we perform a case study using the proapoptotic signal transduction pathway involved in cellular apoptosis.

iHAT: interactive hierarchical aggregation table for genetic association data.

In the search for single-nucleotide polymorphisms which influence the observable phenotype, genome wide association studies have become an important technique for the identification of associations between genotype and phenotype of a diverse set of sequence-based data. We present a methodology for the visual assessment of single-nucleotide polymorphisms using interactive hierarchical aggregation techniques combined with methods known from traditional sequence browsers and cluster heatmaps. Our tool, the interactive Hierarchical Aggregation Table (iHAT), facilitates the visualization of multiple sequence alignments, associated metadata, and hierarchical clusterings. Different color maps and aggregation strategies as well as filtering options support the user in finding correlations between sequences and metadata. Similar to other visualizations such as parallel coordinates or heatmaps, iHAT relies on the human pattern-recognition ability for spotting patterns that might indicate correlation or anticorrelation. We demonstrate iHAT using artificial and real-world datasets for DNA and protein association studies as well as expression Quantitative Trait Locus data.

An eQTL biological data visualization challenge and approaches from the visualization community.

In 2011, the IEEE VisWeek conferences inaugurated a symposium on Biological Data Visualization. Like other domain-oriented Vis symposia, this symposium's purpose was to explore the unique characteristics and requirements of visualization within the domain, and to enhance both the Visualization and Bio/Life-Sciences communities by pushing Biological data sets and domain understanding into the Visualization community, and well-informed Visualization solutions back to the Biological community. Amongst several other activities, the BioVis symposium created a data analysis and visualization contest. Unlike many contests in other venues, where the purpose is primarily to allow entrants to demonstrate tour-de-force programming skills on sample problems with known solutions, the BioVis contest was intended to whet the participants' appetites for a tremendously challenging biological domain, and simultaneously produce viable tools for a biological grand challenge domain with no extant solutions. For this purpose expression Quantitative Trait Locus (eQTL) data analysis was selected. In the BioVis 2011 contest, we provided contestants with a synthetic eQTL data set containing real biological variation, as well as a spiked-in gene expression interaction network influenced by single nucleotide polymorphism (SNP) DNA variation and a hypothetical disease model. Contestants were asked to elucidate the pattern of SNPs and interactions that predicted an individual's disease state. 9 teams competed in the contest using a mixture of methods, some analytical and others through visual exploratory methods. Independent panels of visualization and biological experts judged entries. Awards were given for each panel's favorite entry, and an overall best entry agreed upon by both panels. Three special mention awards were given for particularly innovative and useful aspects of those entries. And further recognition was given to entries that correctly answered a bonus question about how a proposed "gene therapy" change to a SNP might change an individual's disease status, which served as a calibration for each approaches' applicability to a typical domain question. In the future, BioVis will continue the data analysis and visualization contest, maintaining the philosophy of providing new challenging questions in open-ended and dramatically underserved Bio/Life Sciences domains.

Evaluation of traditional, orthogonal, and radial tree diagrams by an eye tracking study.

Node-link diagrams are an effective and popular visualization approach for depicting hierarchical structures and for showing parent-child relationships. In this paper, we present the results of an eye tracking experiment investigating traditional, orthogonal, and radial node-link tree layouts as a piece of empirical basis for choosing between those layouts. Eye tracking was used to identify visual exploration behaviors of participants that were asked to solve a typical hierarchy exploration task by inspecting a static tree diagram: finding the least common ancestor of a given set of marked leaf nodes. To uncover exploration strategies, we examined fixation points, duration, and saccades of participants' gaze trajectories. For the non-radial diagrams, we additionally investigated the effect of diagram orientation by switching the position of the root node to each of the four main orientations. We also recorded and analyzed correctness of answers as well as completion times in addition to the eye movement data. We found out that traditional and orthogonal tree layouts significantly outperform radial tree layouts for the given task. Furthermore, by applying trajectory analysis techniques we uncovered that participants cross-checked their task solution more often in the radial than in the non-radial layouts.