Predictive potential of eigenvalue-based topological molecular descriptors.

This study is directed toward assessing the predictive potential of eigenvalue-based topological molecular descriptors. The graph energy, Estrada index, resolvent energy, and the Laplacian energy were tested as parameters for the prediction of boiling points, heats of formation, and octanol/water partition coefficients of alkanes. It was shown that an eigenvalue-based molecular descriptor cannot be individually used for successful prediction of these physico-chemical properties, but the first Zagreb index, the number of zeros in the spectrum and the number of methyl groups must be also involved in the models. Performed statistics show that the models constructed using the Estrada index and resolvent energy are significantly better than ones with the energy of a graph and the Laplacian energy. Such a trend is even more noticeable in the case of octanol/water partition coefficients of alkanes.

Vapor Pressure, Vaping, and Corrections to Misconceptions Related to Medical Cannabis' Active Pharmaceutical Ingredients' Physical Properties and Compositions.

Medical cannabis products contain dozens of active pharmaceutical ingredients (APIs) derived from the cannabis plant. However, their actual compositions and relative doses significantly change according to the production methods. Product compositions are strongly dependent on processing step conditions and on components' evaporation during those steps. Review of the documentation presented to caregivers and to patients show erroneous data or misinterpretation of data related to the evaporation, for example, cannabinoids' boiling points, as well as confusions between terms, such as boiling, vaporization, and evaporation. Clarifying these aspects is essential for caregivers, for researchers, and for developers of manufacturing processes. Original and literature data were analyzed, comparing composition changes during various processing steps and correlating the extent of change to components' vapor pressures at the corresponding temperature. Evaporation-related composition changes start at temperatures as low as those of drying and curing and become extensive during decarboxylation. The relative rate of components' evaporation is determined by their relative vapor pressure and monoterpenes are lost first. On vaping, terpenes are inhaled before cannabinoids do. Commercial medical cannabis products are deficient in terpenes, mainly monoterpenes, compared with the cannabis plants used to produce them. Terms, such as "whole plant" and "full spectrum," are misleading since no product actually reflects the original cannabis plant composition. There are important implications for medical cannabis manufacturing and for the ability to make the most out of the terpene API contribution. Medical cannabis products' composition and product delivery are controlled by the relative vapor pressure of the various APIs. Quantitative data provided in this study can be used for improvement to reach better accuracy, reproducibility, and preferred medical cannabis compositions.

A Quantitative Structure-Property Relationship (QSPR) Study of aliphatic alcohols by the method of dividing the molecular structure into substructure.

A quantitative structure-property relationship (QSPR) analysis of aliphatic alcohols is presented. Four physicochemical properties were studied: boiling point (BP), n-octanol-water partition coefficient (lg P(OW)), water solubility (lg W) and the chromatographic retention indices (RI) on different polar stationary phases. In order to investigate the quantitative structure-property relationship of aliphatic alcohols, the molecular structure ROH is divided into two parts, R and OH to generate structural parameter. It was proposed that the property is affected by three main factors for aliphatic alcohols, alkyl group R, substituted group OH, and interaction between R and OH. On the basis of the polarizability effect index (PEI), previously developed by Cao, the novel molecular polarizability effect index (MPEI) combined with odd-even index (OEI), the sum eigenvalues of bond-connecting matrix (SX(1CH)) previously developed in our team, were used to predict the property of aliphatic alcohols. The sets of molecular descriptors were derived directly from the structure of the compounds based on graph theory. QSPR models were generated using only calculated descriptors and multiple linear regression techniques. These QSPR models showed high values of multiple correlation coefficient (R > 0.99) and Fisher-ratio statistics. The leave-one-out cross-validation demonstrated the final models to be statistically significant and reliable.

Machine learning transition temperatures from 2D structure.

A priori knowledge of physicochemical properties such as melting and boiling could expedite materials discovery. However, theoretical modeling from first principles poses a challenge for efficient virtual screening of potential candidates. As an alternative, the tools of data science are becoming increasingly important for exploring chemical datasets and predicting material properties. Herein, we extend a molecular representation, or set of descriptors, first developed for quantitative structure-property relationship modeling by Yalkowsky and coworkers known as the Unified Physicochemical Property Estimation Relationships (UPPER). This molecular representation has group-constitutive and geometrical descriptors that map to enthalpy and entropy; two thermodynamic quantities that drive thermal phase transitions. We extend the UPPER representation to include additional information about sp2-bonded fragments. Additionally, instead of using the UPPER descriptors in a series of thermodynamically-inspired calculations, as per Yalkowsky, we use the descriptors to construct a vector representation for use with machine learning techniques. The concise and easy-to-compute representation, combined with a gradient-boosting decision tree model, provides an appealing framework for predicting experimental transition temperatures in a diverse chemical space. An application to energetic materials shows that the method is predictive, despite a relatively modest energetics reference dataset. We also report competitive results on diverse public datasets of melting points (i.e., OCHEM, Enamine, Bradley, and Bergström) comprised of over 47k structures. Open source software is available at https://github.com/USArmyResearchLab/ARL-UPPER.

Quantitative Structure-Property Relationship Study for Prediction of Boiling Point and Enthalpy of Vaporization of Alkenes.

INTRODUCTION: Quantitative structure-property relationships (QSPRs) models have been widely developed to derive a correlation between chemical structures of molecules to their known properties. In this study, QSPR models have been used on 91 alkenes to develop a robust model for the prediction of enthalpy of vaporization under standard condition (ΔH°vap/kJ.mol-1) and at normal temperature of boiling points (T˚bp /K) of alkenes. METHODS: A training set of 81 structurally diverse alkenes was randomly selected and used to construct QSPR models. These models were optimized using backward-multiple linear regression (MLR) analysis. The genetic algorithm and multiple linear regression analysis (GA-MLR) were used to select the suitable descriptors derived from the Dragon software. RESULTS: The multicollinearity properties of the descriptors contributed in the QSPR models were tested and several methods were used for testing the predictive models power such as Leave-One-Out (LOO) cross-validation(Q2 LOO), the five-fold cross-validation techniques, external validation parameters (Q2F1, Q2F2, Q2F3), the concordance correlation coefficient (CCC) and the predictive parameter R2 m. CONCLUSION: The predictive ability of the models was found to be satisfactory, and the five descriptors in three blocks, namely connectivity, edge adjacency indices and 2D matrix-based descriptors could be used to predict the mentioned properties of alkenes.

Modeling molecular boiling points using computed interaction energies.

The noncovalent van der Waals interactions between molecules in liquids are typically described in textbooks as occurring between the total molecular dipoles (permanent, induced, or transient) of the molecules. This notion was tested by examining the boiling points of 67 halogenated hydrocarbon liquids using quantum chemically calculated molecular dipole moments, ionization potentials, and polarizabilities obtained from semi-empirical (AM1 and PM3) and ab initio Hartree-Fock [HF 6-31G(d), HF 6-311G(d,p)], and density functional theory [B3LYP/6-311G(d,p)] methods. The calculated interaction energies and an empirical measure of hydrogen bonding were employed to model the boiling points of the halocarbons. It was found that only terms related to London dispersion energies and hydrogen bonding proved significant in the regression analyses, and the performances of the models generally improved at higher levels of quantum chemical computation. An empirical estimate for the molecular polarizabilities was also tested, and the best models for the boiling points were obtained using either this empirical polarizability itself or the polarizabilities calculated at the B3LYP/6-311G(d,p) level, along with the hydrogen-bonding parameter. The results suggest that the cohesive forces are more appropriately described as resulting from highly localized interactions rather than interactions between the global molecular dipoles.

Solvent additive to achieve highly ordered nanostructural semicrystalline DPP copolymers: toward a high charge carrier mobility.

A facile spin-coating method in which a small percentage of the solvent additive, 1-chloronaphthalene (CN), is found to increase the drying time during film deposition, is reported. The field-effect mobility of a PDPPDBTE film cast from a chloroform-CN mixed solution is 0.46 cm(2) V(-1) s(-1). The addition of CN to the chloroform solution facilitates the formation of highly crystalline polymer structures.