Smell compounds classification using UMAP to increase knowledge of odors and molecular structures linkages

This study aims to highlight the relationships between the structure of smell compounds and their odors. For this purpose, heterogeneous data sources were screened, and 6038 odorant compounds and their known associated odors (162 odor notes) were compiled, each individual molecule being represented with a set of 1024 structural fingerprint. Several dimensional reduction techniques (PCA, MDS, t-SNE and UMAP) with two clustering methods (k-means and agglomerative hierarchical clustering AHC) were assessed based on the calculated fingerprints. The combination of UMAP with k-means and AHC methods allowed to obtain a good representativeness of odors by clusters, as well as the best visualization of the proximity of odorants on the basis of their molecular structures. The presence or absence of molecular substructures has been calculated on odorant in order to link chemical groups to odors. The results of this analysis bring out some associations for both the odor notes and the chemical structures of the molecules such as “woody” and “spicy” notes with allylic and bicyclic structures, “balsamic” notes with unsaturated rings, both “sulfurous” and “citrus” with aldehydes, alcohols, carboxylic acids, amines and sulfur compounds, and “oily”, “fatty” and “fruity” characterized by esters and with long carbon chains. Overall, the use of UMAP associated to clustering is a promising method to suggest hypotheses on the odorant structure-odor relationships.

Solid solutions of flexible host−guest supramolecules for tuning molecular motion and phase transition

By utilizing integrated supramolecular complex rather than individual molecule as deformable and elastic substitutional component, we demonstrate an elegant example of how two related yet non-isostructural crystalline host−guest compounds can...

The Coffee–Acrylamide Apparent Paradox: An Example of Why the Health Impact of a Specific Compound in a Complex Mixture Should Not Be Evaluated in Isolation

The health implications of acrylamide in food are a matter of concern based on toxicological studies in rodents, which showed that doses of acrylamide more than 100 times higher than those estimated to result from dietary exposure in humans are carcinogenic; however, the cancer types reported in rodents are species-specific, and whether these results can be extrapolated to humans is still in question. In fact, human epidemiological studies revealed a general lack of association between dietary acrylamide exposure and the incidence of different cancer types. Even occupational exposure to acrylamide, resulting in acrylamide exposure nearly 10 times higher than dietary exposure, did not increase tumor occurrence. Furthermore, the consumption of coffee, which is a main contributor of dietary acrylamide exposure, actually decreases the overall incidence of cancer in humans and afford global health benefits, increasing both lifespan and healthspan on ageing. This paradox clearly illustrates the risk of evaluating an individual molecule independently of its complete food matrix, which may have other components that completely override the effects of the considered molecule.

Comprehensive Analysis of Applicability Domains of QSPR Models for Chemical Reactions

Nowadays, the problem of the model’s applicability domain (AD) definition is an active research topic in chemoinformatics. Although many various AD definitions for the models predicting properties of molecules (Quantitative Structure-Activity/Property Relationship (QSAR/QSPR) models) were described in the literature, no one for chemical reactions (Quantitative Reaction-Property Relationships (QRPR)) has been reported to date. The point is that a chemical reaction is a much more complex object than an individual molecule, and its yield, thermodynamic and kinetic characteristics depend not only on the structures of reactants and products but also on experimental conditions. The QRPR models’ performance largely depends on the way that chemical transformation is encoded. In this study, various AD definition methods extensively used in QSAR/QSPR studies of individual molecules, as well as several novel approaches suggested in this work for reactions, were benchmarked on several reaction datasets. The ability to exclude wrong reaction types, increase coverage, improve the model performance and detect Y-outliers were tested. As a result, several “best” AD definitions for the QRPR models predicting reaction characteristics have been revealed and tested on a previously published external dataset with a clear AD definition problem.

The Dynamic State of a Pseudo-Crystalline Structure of B42 Molecules

In this paper, we have developed a mathematical model of the dynamics of molecular structures arising from an approach using B42 molecules. The model is based on equations that determine the motion of the centers of mass of the molecules in question, and equations for the projections of the moments of quantities of motion of these particles on the axes associated with them. The thermal motions of boron atoms within each individual molecule are calculated using a bond-ordered potential. The intermolecular interactions of atoms have a potential that appears to coincide with that of free atoms. However, the interaction parameters differ. The obtained columns of molecular disks for B42 are stable structures. We determine the characteristic vibration amplitudes and rotation frequency of the molecules in question.

HITRAN2020: An overview of what to expect

<p>The HITRAN database is an integral component of numerous atmospheric radiative transfer models and it is therefore essential that the database contains the most appropriate up-to-date spectroscopic parameters. To this end, the HITRAN2020 database is scheduled to be released at the end of this year.  The compilation of this edition (as is the tradition for the HITRAN database) exemplifies the efficiency and necessity of worldwide scientific collaborations. It is a titanic effort of experimentalists, theoreticians and atmospheric scientists, who measure, calculate and validate the HITRAN data.</p><p>The HITRAN line-by-line lists for almost all 49 molecules have been updated in comparison to HITRAN2016 (Gordon et al., 2017), the previous compilation. The extent of these updates depend on the molecule, but range from small adjustments for a few lines of an individual molecule to complete replacements of line lists and the introduction of new isotopologues. Many new vibrational bands have been added to the database, thereby extending the spectral coverage and completeness of the datasets. In addition the accuracy of the parameters for major atmospheric absorbers has been substantially increased, often featuring sub-percent uncertainties.</p><p>Furthermore, the amount of parameters has also been significantly increased. For example, HITRAN2020 will now incorporate non-Voigt line profiles for many gases, broadening by water vapour (Tan et al., 2019), as well as updated collision induced absorption sets (Karman et al., 2019). The HITRAN2020 edition will continue taking advantage of the new structure and interface available at www.hitran.org (Hill et al., 2016) and the HITRAN Application Programming Interface (Kochanov et al., 2016).</p><p>This talk will provide a summary of these updates, emphasizing details of some of the most important or drastic improvements.</p><p><strong>References:</strong></p><p>Gordon, I.E., .et al., (2017), <em>JQSRT</em> <strong>203</strong>, 3–69.  (doi:10.1016/j.jqsrt.2017.06.038)</p><p>Hill, C., et al., (2016), <em>JQSRT</em> <strong>177</strong>, 4–14.  (doi:10.1016/j.jqsrt.2015.12.012)</p><p>Karman, T., et al. (2019), <em>Icarus</em> <strong>328</strong>, 160–175.  (doi:10.1016/j.icarus.2019.02.034)</p><p>Kochanov, R.V., et al.,( 2016), <em>JQSRT</em> <strong>177</strong>, 15–30.  (doi:10.1016/j.jqsrt.2016.03.005)</p><p>Tan, Y., et al., (2019),<em> J. Geophys. Res. Atmos.</em> <strong>124</strong>, 11580-11594. (doi:10.1029/2019JD030929)</p><p> </p>

A Bayesian nonparametric approach to super-resolution single-molecule localization

We consider the problem of single-molecule identification in super-resolution microscopy. Super-resolution microscopy overcomes the diffraction limit by localizing individual fluorescing molecules in a field of view. This is particularly difficult since each individual molecule appears and disappears randomly across time and because the total number of molecules in the field of view is unknown. Additionally, data sets acquired with super-resolution microscopes can contain a large number of spurious fluorescent fluctuations caused by background noise.To address these problems, we present a Bayesian nonparametric framework capable of identifying individual emitting molecules in super-resolved time series. We tackle the localization problem in the case in which each individual molecule is already localized in space. First, we collapse observations in time and develop a fast algorithm that builds upon the Dirichlet process. Next, we augment the model to account for the temporal aspect of fluorophore photo-physics. Finally, we assess the performance of our methods with ground-truth data sets having known biological structure.