FP-MAP: an extensive library of fingerprint-based molecular activity prediction tools.

Discovering new drugs for disease treatment is challenging, requiring a multidisciplinary effort as well as time, and resources. With a view to improving hit discovery and lead compound identification, machine learning (ML) approaches are being increasingly used in the decision-making process. Although a number of ML-based studies have been published, most studies only report fragments of the wider range of bioactivities wherein each model typically focuses on a particular disease. This study introduces FP-MAP, an extensive atlas of fingerprint-based prediction models that covers a diverse range of activities including neglected tropical diseases (caused by viral, bacterial and parasitic pathogens) as well as other targets implicated in diseases such as Alzheimer's. To arrive at the best predictive models, performance of ≈4,000 classification/regression models were evaluated on different bioactivity data sets using 12 different molecular fingerprints. The best performing models that achieved test set AUC values of 0.62-0.99 have been integrated into an easy-to-use graphical user interface that can be downloaded from https://gitlab.com/vishsoft/fpmap.

Drugsniffer: An Open Source Workflow for Virtually Screening Billions of Molecules for Binding Affinity to Protein Targets.

The SARS-CoV2 pandemic has highlighted the importance of efficient and effective methods for identification of therapeutic drugs, and in particular has laid bare the need for methods that allow exploration of the full diversity of synthesizable small molecules. While classical high-throughput screening methods may consider up to millions of molecules, virtual screening methods hold the promise of enabling appraisal of billions of candidate molecules, thus expanding the search space while concurrently reducing costs and speeding discovery. Here, we describe a new screening pipeline, called drugsniffer, that is capable of rapidly exploring drug candidates from a library of billions of molecules, and is designed to support distributed computation on cluster and cloud resources. As an example of performance, our pipeline required ∼40,000 total compute hours to screen for potential drugs targeting three SARS-CoV2 proteins among a library of ∼3.7 billion candidate molecules.

Evaluation of Molecular Fingerprints for Determining Dye Aggregation on Semiconductor Surfaces.

Dye aggregation plays an important role in determining the photovoltaic performance of dye sensitized solar cells. Compared with the spectra observed in solution, it is, apriori, difficult to ascertain whether a dye is likely to show hypsochromic (H) or bathochromic (J) aggregation, until after adsorption onto the semiconductor electrode. Herein, we show that molecular fingerprint-based methods provide a fast and efficient way to discriminate between H- and J-aggregating dyes. The efficacy of the fingerprint-based classification models is demonstrated with a diverse set of over 3000 organic dyes dissolved in different solvents. Requiring only the structure of the dye and the polarity of the solvent used, the machine learning model achieves close to 80 % classification accuracies that are comparable with models based on a combination of fragment counts and topological indices. For interested researchers, we have bundled the prediction tools as an R package.

Systematic assessment of salivary inflammatory markers and dental caries in children: an exploratory study.

OBJECTIVE: To investigate associations between a wide panel of salivary inflammatory markers and the presence of dental caries among children. MATERIAL AND METHODS: In this exploratory, cross-sectional study, 176 children, aged 7-9, underwent a dental examination. Information on the children's oral health habits and lifestyles was collected from their mothers. In addition, saliva samples were collected and analyzed using a multiplex immunoassay. Of 92 inflammatory markers measured, 56 were included in the statistical analyses. To identify potential inflammatory markers associated with caries, we applied low to advanced statistical analyses. First, we performed traditional logistic regression analysis followed by Bonferroni corrections. Thereafter, a more robust and less conservative statistical approach, i.e. Least Absolute Shrinkage and Selection Operator (LASSO), was applied. The models were adjusted for potential confounders. RESULTS: Of the 176 children in the study, 22.2% were affected by caries. Among the 56 salivary inflammatory markers, only macrophage colony-stimulating factor 1 (CSF1) was selected by the LASSO and found to be positively associated with the presence of caries. CONCLUSIONS: The observed association between CSF1 and the presence of caries may be of clinical value in caries risk management and early diagnosis. Larger studies are warranted to assess the replicability of our findings.

Occurrence of bisphenols and benzophenone UV filters in wild brown mussels (Perna perna) from Algoa Bay in South Africa.

Bisphenols and benzophenone UV filters are contaminants present in a wide variety of plastic materials and consumer products. The scientific attention towards these contaminants has increased in recent years due to their presence in microplastics, their ubiquitous occurrence in the environment, and their known endocrine disrupting health effects. In this study, the occurrence of nine bisphenol and five benzophenone UV-filter analogues was assessed in wild brown mussels (Perna perna) collected from different sampling sites along the coast of Algoa Bay, South Africa. Eleven out of fourteen target analytes were detected, and bisphenol AP (BPAP) was detected for the first time in mussels, presenting the highest median concentration of 150 ng g-1 dry weight (d.w.) and a detection frequency of 91%. Regarding benzophenone UV-filters, median concentrations of the analogues (across all sites) ranged from 2.01 to 10.6 ng g-1 d.w., with benzophenone-1 (BzP-1) and benzophenone-3 (BzP-3) presenting the highest concentrations. Human exposure was assessed by estimating daily intakes (EDI) of the target analytes through mussel consumption. To our knowledge, this is the first study from the African continent on the occurrence of bisphenols and benzophenone UV-filters in a large population (n=138) of wild brown mussels.

FP-ADMET: a compendium of fingerprint-based ADMET prediction models.

MOTIVATION: The absorption, distribution, metabolism, excretion, and toxicity (ADMET) of drugs plays a key role in determining which among the potential candidates are to be prioritized. In silico approaches based on machine learning methods are becoming increasing popular, but are nonetheless limited by the availability of data. With a view to making both data and models available to the scientific community, we have developed FPADMET which is a repository of molecular fingerprint-based predictive models for ADMET properties. In this article, we have examined the efficacy of fingerprint-based machine learning models for a large number of ADMET-related properties. The predictive ability of a set of 20 different binary fingerprints (based on substructure keys, atom pairs, local path environments, as well as custom fingerprints such as all-shortest paths) for over 50 ADMET and ADMET-related endpoints have been evaluated as part of the study. We find that for a majority of the properties, fingerprint-based random forest models yield comparable or better performance compared with traditional 2D/3D molecular descriptors. AVAILABILITY: The models are made available as part of open access software that can be downloaded from https://gitlab.com/vishsoft/fpadmet .

Adaptation of central metabolite pools to variations in growth rate and cultivation conditions in Saccharomyces cerevisiae.

BACKGROUND: Saccharomyces cerevisiae is a well-known popular model system for basic biological studies and serves as a host organism for the heterologous production of commercially interesting small molecules and proteins. The central metabolism is at the core to provide building blocks and energy to support growth and survival in normal situations as well as during exogenous stresses and forced heterologous protein production. Here, we present a comprehensive study of intracellular central metabolite pool profiling when growing S. cerevisiae on different carbon sources in batch cultivations and at different growth rates in nutrient-limited glucose chemostats. The latest versions of absolute quantitative mass spectrometry-based metabolite profiling methodology were applied to cover glycolytic and pentose phosphate pathway metabolites, tricarboxylic acid cycle (TCA), complete amino acid, and deoxy-/nucleoside phosphate pools. RESULTS: Glutamate, glutamine, alanine, and citrate were the four most abundant metabolites for most conditions tested. The amino acid is the dominant metabolite class even though a marked relative reduction compared to the other metabolite classes was observed for nitrogen and phosphate limited chemostats. Interestingly, glycolytic and pentose phosphate pathway (PPP) metabolites display the largest variation among the cultivation conditions while the nucleoside phosphate pools are more stable and vary within a closer concentration window. The overall trends for glucose and nitrogen-limited chemostats were increased metabolite pools with the increasing growth rate. Next, comparing the chosen chemostat reference growth rate (0.12 h-1, approximate one-fourth of maximal unlimited growth rate) illuminates an interesting pattern: almost all pools are lower in nitrogen and phosphate limited conditions compared to glucose limitation, except for the TCA metabolites citrate, isocitrate and α-ketoglutarate. CONCLUSIONS: This study provides new knowledge-how the central metabolism is adapting to various cultivations conditions and growth rates which is essential for expanding our understanding of cellular metabolism and the development of improved phenotypes in metabolic engineering.

Rapid determination of thyroid hormones in blood plasma from Glaucous gulls and Baikal seals by HybridSPE®-LC-MS/MS.

A rapid hybrid solid phase extraction (HybridSPE®) protocol tailored to liquid chromatography-electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) analysis, was developed for the determination of four thyroid hormones, L-Thyroxine (T4), 3,3',5-triiodo-L-thyronine (T3), 3,3',5'-triiodo-L-thyronine (rT3) and 3,3'-diiodo-L-thyronine (T2) in blood plasma from Glaucous gulls (Larus hyperboreus) and Baikal seals (Phoca sibirica). The use of target analyte specific 13C internal standards allowed quantification to be performed through the standard solvent calibration curves and alleviated the need to perform quantification with matrix match curves. The relative recoveries were 100.0-110.1 % for T4, 99.1-102.2 % for T3, 100.5-108.0 % for rT3, and 100.5-104.6 % for T2. The matrix effects ranged from -1.52 to -6.10 %, demonstrating minor signal suppression during analysis. The method intra-day precision (method repeatability, RSD %, N = 5, k = 1 day) and inter-day precision (method reproducibility, RSD %, N = 10, k = 2 days) at the 1 ng/mL concentration of fortification were 8.54-15.4 % and 15.4-24.8 %, respectively, indicating acceptable chromatographic peak stabilities for all target THs even at trace level concentrations. The method limit of detection (LOD) for T4, T3, rT3 and T2 was 0.17, 0.16, 0.30 and 0.17 ng/mL, respectively. The HybridSPE® protocol was simple and rapid (<1 min) upon application, while the HybridSPE® cartridge did not require (as in classical SPE cartridges) any additional equilibration nor conditioning step prior sample loading. A total of 46 blood plasma samples, 30 samples collected from Glaucous gulls and 16 samples collected from Baikal seals, were analyzed for thyroid hormones to demonstrate the applicability of the developed method in these wildlife species. The concentrations of T4 and T3 in blood plasma from the Glaucous gulls were 5.95-44.2 and 0.37-5.61 ng/mL, respectively, whereas those from Baikal seals were 3.57-46.5 and 0.45-2.07 ng/mL, respectively. In both species, rT3 demonstrated low detection rate, while T2 was not detected. Furthermore, cross-array comparison between the HybridSPE®-LC-MS/MS protocol and an established routine radioimmunoassay (RIA) kit-based method was performed for T4 and T3 concentrations from selected Baikal seal plasma samples.

Synthesis of Novel 3,6-Dithienyl Diketopyrrolopyrrole Dyes by Direct C‒H Arylation.

Direct C‒H arylation coupling is potentially a more economical and sustainable process than conventional cross-coupling. However, this method has found limited application in the synthesis of organic dyes for dye‒sensitized solar cells. Although direct C‒H arylation is not an universal solution to any cross-coupling reactions, it efficiently complements conventional sp2‒sp2 bond formation and can provide shorter and more efficient routes to diketopyrrolopyrrole dyes. Here, we have applied palladium catalyzed direct C‒H arylation in the synthesis of five new 3,6-dithienyl diketopyrrolopyrrole dyes. All prepared sensitizers display broad absorption from 350 nm up to 800 nm with high molar extinction coefficients. The dye‒sensitized solar cells based on these dyes exhibit a power conversion efficiency in the range of 2.9 to 3.4%.

Chemical assessment of marine sediments in vicinity of Norwegian fish farms - A pilot study.

While aquaculture is growing rapidly all over the world and generating many economic benefits, so have the environmental concerns about the externalities posed by the fish-farming industry. The distribution profiles of organic compounds and inorganic elements were explored in marine surface sediments collected in proximity of two active Norwegian fish farms, Hestøya and Nørholmen (<200-1100 m from the perimeter edge of the installations). Overall, the sediment organic matter (SOM) content was 7.3 ± 4.9%, with 7.9 ± 5.1% and 4.0 ± 0.5% for Hestøya and Nørholmen, respectively. A non-targeted analysis was performed for screening organic compounds in marine sediments, and the presence of 60 compounds was detected. Among suspect compounds were alkanes, alkenes, aromatics, aldehydes, ketones, esters, alcohols, diols, polycyclic aromatic hydrocarbons (PAHs), terpenes and terpenoids. Heptanal, benzaldehyde, 4-oxoisophorone, 1,7-dimethylnaphthalene and 3-bromophenol were the most abundant compounds in marine sediments. In total, concentrations of 47 elements were measured, concentrations of As, Cd, Cr, Cu, Hg, Mo, Ni, Sn and Zn were strongly influenced by anthropogenic inputs, while concentrations of Ce, Co, Al, Fe and Ti were related to the geology of the local bedrock. The chemical composition of marine sediments was different at Hestøya and Nørholmen, indicating different anthropogenic inputs in these areas. In general, concentrations of toxic elements were below the proposed guidelines for Norwegian marine sediment quality and can be characterised as background pollution. Overall, fish-farming activities had only a minor or negligible influence on marine sediments and are unlikely to cause any harm to local aquatic life in the studied area.

Phthalate metabolites in harbor porpoises (Phocoena phocoena) from Norwegian coastal waters.

The exposure of marine mammals to phthalates has received considerable attention due to the ubiquitous occurrence of these pollutants in the marine environment and their potential adverse health effects. The occurrence of phthalate metabolites is well established in human populations, but data is scarce for marine mammals. In this study, concentrations of 17 phthalate metabolites were determined in liver samples collected from one hundred (n = 100) by-caught harbor porpoises (Phocoena phocoena) along the coast of Norway. Overall, thirteen phthalate metabolites were detected in the samples. Monoethyl phthalate (mEP), mono-iso-butyl phthalate (mIBP), mono-n-butyl phthalate (mBP) and phthalic acid (PA) were the most abundant metabolites, accounting for detection rates ≥ 85%. The highest median concentrations were found for mIBP (30.6 ng/g wet weight [w.w.]) and mBP (25.2 ng/g w.w.) followed by PA (7.75 ng/g w.w.) and mEP (5.67 ng/g w.w.). The sum of the median phthalate metabolites concentrations that were found in the majority of samples (detection rates > 50%) indicated that concentrations were lower for porpoises collected along the coastal area of Bodø (Nordland), Lebesby (Finnmark) and Varangerfjord (as compared to other coastal areas); these areas are among the least populated coastal areas but also the most distant (>700 km) from offshore active oil and gas fields. The monomethyl phthalate metabolite (mMP) was detected in 69% of the samples, and to our knowledge, alongside with PA, this is the first report of their occurrence in marine mammals. PA, as the non-specific marker of phthalate exposures, showed a statistically significant negative association with the body mass and length of the harbor porpoises. Among the phthalate metabolites, statistically significant positive associations were found between mBP and mIBP, mMP and mEP, PA and mEP, mIBP and mono(2-ethyl-5-oxohexyl) phthalate (mEOHP), mIBP and mono(2-ethyl-5-hydroxyhexyl) phthalate (mEHHP), mBP and mEHHP, mono-n-nonyl phthalate (mNP) and PA, and between monobenzyl phthalate (mBzP) and mNP. To our knowledge, this is the first study on the biomonitoring of 17 phthalate metabolites in harbor porpoises.

Highly efficient cellulose dissolution by alkaline ionic liquids.

Alkaline ionic liquids (ILs) with unconventional organic anions were prepared and used for cellulose dissolution studies. High concentrations of cellulose were dissolved at room temperature in the phenolate based imidazolium IL [C2mim][OPh], combined with organic solvent, and up to 45 wt-% cellulose dissolution (wt-% MCC of weight IL) was readily achieved at 100 ºC. No functionalization of the regenerated cellulose was observed during the dissolution process (FTIR). Characteristic cellulose II XRD diffraction pattern was observed after IL dissolution and regeneration of MCC. The crystallinity index (CI) of the pretreated MCC was reduced from 93.2 % to 31 %. Inert conditions were not required for the cellulose dissolution experiments. This study indicates that the IL H-bond basicity is not the only key parameter determining their cellulose dissolution ability. The alkaline ILs represent an energy efficient and sustainable approach for cellulose dissolution.

Prediction of Absorption Spectrum Shifts in Dyes Adsorbed on Titania.

Dye adsorption on metal-oxide films often results in small to substantial absorption shifts relative to the solution phase, with undesirable consequences for the performance of dye-sensitized solar cells and optical sensors. While density functional theory is frequently used to model such behaviour, it is too time-consuming for rapid assessment. In this paper, we explore the use of supervised machine learning to predict whether dye adsorption on titania is likely to induce a change in its absorption characteristics. The physicochemical features of each dye were encoded as a numeric vector whose elements are the counts of molecular fragments and topological indices. Various classification models were subsequently trained to predict the type of absorption shift i.e. blue, red or unchanged (|Δλ| ≤ 10 nm). The models were able to predict the nature of the shift with a good likelihood (~80%) of success when applied to unseen data.

Hydroxyl Functionalized Pyridinium Ionic Liquids: Experimental and Theoretical Study on Physicochemical and Electrochemical Properties.

Structurally modified hydroxyl functionalized pyridinium ionic liquids (ILs), liquid at room temperature, were synthesized and characterized. Alkylated N-(2-hydroxyethyl)-pyridinium ILs were prepared from alkylpyridines via corresponding bromide salts by N-alkylation (65-93%) and final anion exchange (75-96%). Pyridinium-alkylation strongly influenced the IL physicochemical and electrochemical properties. Experimental values for the ILs physicochemical properties (density, viscosity, conductivity, and thermal decomposition temperature), were in good agreement with corresponding predicted values obtained by theoretical calculations. The pyridinium ILs have electrochemical window of 3.0-5.4 V and were thermally stable up to 405°C. The IL viscosity and density were measured over a wide temperature range (25-80°C). Pyridine alkyl-substitution strongly affected the partial positive charge on the nitrogen atom of the pyridinium cations, as shown by charge distribution calculations. On-going studies on Mg complexes of the new ILs demonstrate promising properties for high current density electrodeposition of magnesium.

Establishing Predictive Models for Solvatochromic Parameters of Ionic Liquids.

The use of ionic liquids (ILs) in applications ranging from catalysis to reaction media in organic synthesis has been successfully demonstrated in several cases. For any given IL application, fundamental properties, such as viscosity, thermal stability, and toxicity have to be considered. Another property of interest is the polarity, which is a crucial indicator of solvent effects on chemical processes. Given the near-infinite combinations of cations and anions, experimental determination of solvatochromic parameters, such as the hydrogen-bond acidity and basicity, and dipolarity-polarizability is prohibitive. To address this, we evaluate the utility of alternative schemes based on parameters derived from COSMO-RS (COnductor-like Screening MOdel for Real Solvents) computations. The scheme is applied to a large library of yet-to-be-synthesized ionic liquids, to identify promising candidates for applications in biomass dissolution.

DENOPTIM: Software for Computational de Novo Design of Organic and Inorganic Molecules.

A general-purpose software package, termed DE Novo OPTimization of In/organic Molecules (DENOPTIM), for de novo design and virtual screening of functional molecules is described. Molecules of any element and kind, including metastable species and transition states, are handled as chemical objects that go beyond valence-rules representations. Synthetic accessibility of the generated molecules is ensured via detailed control of the kinds of bonds that are allowed to form in the automated molecular building process. DENOPTIM contains a combinatorial explorer for screening and a genetic algorithm for global optimization of user-defined properties. Estimates of these properties may be obtained to form the fitness function (figure of merit or scoring function) from external molecular modeling programs via shell scripts. Examples of a range of different fitness functions and DENOPTIM applications, including an easy-to-do test case, are described. DENOPTIM is available as Open Source from https://github.com/denoptim-project/DENOPTIM .

Benzotriazoles, benzothiazoles and trace elements in an urban road setting in Trondheim, Norway: Re-visiting the chemical markers of traffic pollution.

Road traffic emissions are known to contribute heavily to the pollution in urban environments. The aim of this study was to establish specific traffic pollution markers in an urban road setting based on the occurrence profiles of benzotriazoles, benzothiazoles and trace elements in road dust and relevant matrices, including airborne particulate matter and core asphalt. Benzotriazoles and benzothiazoles are high-production volume chemicals that are used as complexing and anticorrosive agents for metals, act as vulcanizing accelerators for rubber materials, and possess anti-freezing/anti-icing properties. In this study, six benzothiazoles (benzothiazole, 2­morpholin­4­yl­benzothiazole, 2­hydroxy­benzothiazole, 2­thio­benzothiazole, 2­methylthio­benzothiazole, and 2­amino­benzothiazole), seven benzotriazoles (1H­benzotriazole, 1­hydroxy­benzotriazole, 5­chloro­1H­benzotriazole, tolyltriazole, xylyltriazole, benzotriazole­5­carboxyl acid, and 5­amino­1H­benzotriazole), and 66 trace elements were determined in road dust samples from a sub-arctic urban road setting in Norway, and seasonal occurrence profiles were assessed between the studded and the non-studded tire season. The road dust was collected as suspended particulate matter in an aqueous phase with the introduced dust sampler in Scandinavia, the Wet Dust Sampler. The concentrations of the sum of seven benzotriazoles (Σ(7)BTRs) and six benzothiazoles (Σ(6)BTHs) in road dust ranged from 191 to 3054 ng/L and 93.4 to 1903 ng/L, respectively. To the best of our knowledge, 1H­benzotriazole and tolyltriazole are reported for the first time as suitable markers of metal corrosion in vehicles. From the benzothiazole class, 2­thio­benzothiazole was found to be a suitable marker of tire rubber particles, while its methylated derivative, 2­methylthio­benzothiazole, was found to be a marker of chemical leaching. In addition, different types of new unused tires (summer, studded, and non-studded) were analyzed to assess their benzothiazoles and benzotriazoles content. Based on the concentrations found for benzotriazoles and benzothiazoles in airborne particulate matter, human exposure doses were calculated, and the estimated daily intake doses were found on the order of picograms per day.

Local initiation conditions for water autoionization.

The pH of liquid water is determined by the infrequent process in which water molecules split into short-lived hydroxide and hydronium ions. This reaction is difficult to probe experimentally and challenging to simulate. One of the open questions is whether the local water structure around a slightly stretched OH bond is actually initiating the eventual breakage of this bond or whether this event is driven by a global ordering that involves many water molecules far away from the reaction center. Here, we investigated the self-ionization of water at room temperature by rare-event ab initio molecular dynamics and obtained autoionization rates and activation energies in good agreement with experiments. Based on the analysis of thousands of molecular trajectories, we identified a couple of local order parameters and show that if a bond stretch occurs when all these parameters are around their ideal range, the chance for the first dissociation step (double-proton jump) increases from [Formula: see text] to 0.4. Understanding these initiation triggers might ultimately allow the steering of chemical reactions.

The dye-sensitized solar cell database.

BACKGROUND: Dye-sensitized solar cells (DSSCs) have garnered a lot of attention in recent years. The solar energy to power conversion efficiency of a DSSC is influenced by various components of the cell such as the dye, electrolyte, electrodes and additives among others leading to varying experimental configurations. A large number of metal-based and metal-free dye sensitizers have now been reported and tools using such data to indicate new directions for design and development are on the rise. DESCRIPTION: DSSCDB, the first of its kind dye-sensitized solar cell database, aims to provide users with up-to-date information from publications on the molecular structures of the dyes, experimental details and reported measurements (efficiencies and spectral properties) and thereby facilitate a comprehensive and critical evaluation of the data. Currently, the DSSCDB contains over 4000 experimental observations spanning multiple dye classes such as triphenylamines, carbazoles, coumarins, phenothiazines, ruthenium and porphyrins. CONCLUSION: The DSSCDB offers a web-based, comprehensive source of property data for dye sensitized solar cells. Access to the database is available through the following URL: www.dyedb.com .

Designing High-Refractive Index Polymers Using Materials Informatics.

A machine learning strategy is presented for the rapid discovery of new polymeric materials satisfying multiple desirable properties. Of particular interest is the design of high refractive index polymers. Our in silico approach employs a series of quantitative structure⁻property relationship models that facilitate rapid virtual screening of polymers based on relevant properties such as the refractive index, glass transition and thermal decomposition temperatures, and solubility in standard solvents. Exploration of the chemical space is carried out using an evolutionary algorithm that assembles synthetically tractable monomers from a database of existing fragments. Selected monomer structures that were further evaluated using density functional theory calculations agree well with model predictions.