ARTIFICIAL INTELLIGENCE IN SYNTHETIC CHEMISTRY: ACHIEVEMENTS AND PERSPECTIVES

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CHEMICAL SPACE ANALYSIS OF SN2 REACTIONS IN SOLUTION BASED ON THE CONDENSED REACTION GRAPH APPROACH

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ARTIFICIAL INTELLIGENCE IN SYNTHETIC CHEMISTRY: ACHIEVEMENTS AND PERSPECTIVES

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Oxygen uptake rates have contrasting responses to temperature in the root meristem and elongation zone

Growing at either 15 or 25°C, roots of Arabidopsis thaliana, Columbia accession, produce cells at the same rate and have growth zones of the same length. To determine whether this constancy is related to energetics, we measured oxygen uptake by means of a vibrating oxygen-selective electrode. Concomitantly, the spatial distribution of elongation was measured kinematically, delineating meristem and elongation zone. All seedlings were germinated, grown, and measured at a given temperature(15 or 25°C). Columbia was compared to lines where cell production rate roughly doubles between 15 and 25°C: Landsberg and two Columbia mutants,er-105andahk3-3. For all genotypes and temperatures, oxygen uptake rate at any position was highest at the root cap, where mitochondrial density was maximal, based on the fluorescence of a reporter. Uptake rate declined through the meristem to plateau within the elongation zone. For oxygen uptake rate integrated over a zone, the meristem had steady-stateQ10values ranging from 0.7 to 2.1; by contrast, the elongation zone had values ranging from 2.6 to 3.3, implying that this zone exerts a greater respiratory demand. These results highlight a substantial energy consumption by the rootcap, perhaps helpful for maintaining hypoxia in stem cells, and suggest that rapid elongation is metabolically more costly than is cell division.Maura J. Zimmermann, Jayakumar Bose, Eric M. Kramer, Owen K. Atkin, Stephen D. Tyerman, Tobias I. Baski

Comprehensive analysis of applicability domains of QSPR models for chemical reactions

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. 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

Machine learning modelling of chemical reaction characteristics: yesterday, today, tomorrow

The synthesis of the desired chemical compound is the main task of synthetic organic chemistry. The predictions of reaction conditions and some important quantitative characteristics of chemical reactions as yield and reaction rate can substantially help in the development of optimal synthetic routes and assessment of synthesis cost. Theoretical assessment of these parameters can be performed with the help of modern machine-learning approaches, which use available experimental data to develop predictive models called quantitative or qualitative structure–reactivity relationship (QSRR) modelling. In the article, we review the state-of-the-art in the QSRR area and give our opinion on emerging trends in this field

CHEMICAL SPACE ANALYSIS OF SN2 REACTIONS IN SOLUTION BASED ON THE CONDENSED REACTION GRAPH APPROACH

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