Generalized Molecular Descriptors Derived From Event-Based Discrete Derivative.

A new procedure (GENLOIS) is presented for generating trees or certain classes of trees such as 4-trees (graphs representing alkanes), identity trees, homeomorphical irreducible trees, rooted trees, trees labelled on a certain vertex (primary, secondary, tertiary, etc.). The present method differs from previous procedures by differentiating among the vertices of a given parent graph by means of local vertex invariants (LOVIs). New graphs are efficiently generated by adding points and/or edges only to non-equivalent vertices of the parent graph. Redundant generation of graphs is minimized and checked by means of highly discriminating, recently devised topological indices based either on LOVIs or on the information content of LOVIs. All trees onN + 1 (N + 1 < 17) points could thus be generated from the complete set of trees onN points. A unique cooperative labelling for trees results as a consequence of the generation scheme. This labelling can be translated into a code for which canonical rules were recently stated by A.T. Balaban. This coding appears to be one of the best procedures for encoding, retrieving or ordering the molecular structure of trees (or alkanes).

Chemical structures of organic compounds are characterized numerically by a variety of structural descriptors, one of the earliest and most widely used being the Wiener index W, derived from the interatomic distances in a molecular graph. Extensive use of distance-based structural descriptors or topological indices has been made in QSPR and QSAR models, drug design, toxicology, virtual screening of combinatorial libraries, similarity and diversity assessment. Novel topological indices are introduced representing a partitioning of the Wiener polynomial based on counts of even and odd molecular graph distances. During the QSAR/QSPR modeling process the variables of the even and odd power functions are optimized in order to offer an improved mapping of the investigated property. These novel topological indices are tested in QSPR models for the boiling temperature, molar heat capacity, standard Gibbs energy of formation, vaporization enthalpy, refractive index, and density of alkanes. In many cases, the even/odd Wiener polynomial indices proposed here give notably improved correlations or suggest simpler QSPR models.

QSPR prediction of N-boiling point and critical properties of organic compounds and comparison with a group-contribution method

The tri-n-octylmethyl-ammonium methyl carbonate was synthesized via a high pressure process with tri-n-octylamine(tri-C8) and dimethyl carbonate(DMC) in the catalysis of tri-n-octylmethyl-ammonium bromine, and its chemical structure was confirmed using FTIR spectroscopic analysis. The influence of reactants molar ratio, reaction temperature, solvent, catalyst and reaction time on the quaternization reaction were examined, and the optimal conditions were that dimethyl carbonate(DMC):tri-n-octylamine(tri-C8)=5.6:1(molar:molar), methanol:tri-n-octylamine (tri-C8)=1:2 (volume:volume), catalysts 5% of the weight of reactant mixtures(materials fraction), reaction temperature 110°C and reaction time 8 h. Under the optimum conditions, the highest conversion rate of tri-n-octylamine(tri-C8) could reach 99.57%. A type of SO42- quaternary ammonium salt was prepared by the reaction between the product and H2SO4, and its properties on extracting vanadium in the feed solution from autoclave-soda leach solution of stone coal was researched, demonstrating that with 8% the type of SO42- quaternary ammonium salt(QAS) and 5% sec-octyl alcohols dissolved in 87% sulphonated kerosene as extractant (volume fraction), the saturated loading capacity for V2O5 reaches 14.32 g/L.

Identifying the potential side effects of drugs is crucial in clinical trials in the pharmaceutical industry. The existing side effect prediction methods mainly focus on the chemical and biological properties of drugs. This study proposes a method that uses diverse information such as drug-drug interactions from DrugBank, drug-drug interactions from network, single nucleotide polymorphisms, and side effect anatomical hierarchy as well as chemical structures, indications, and targets. The proposed method is based on the assumption that properties used in drug repositioning studies could be utilized to predict side effects because the phenotypic expression of a side effect is similar to that of the disease. The prediction results using the proposed method showed a 3.5% improvement in the area under the curve (AUC) over that obtained when only chemical, indication, and target features were used. The random forest model delivered outstanding results for all combinations of feature types. Finally, after identifying candidate side effects of drugs using the proposed method, the following four popular drugs were discussed: (1) dasatinib, (2) sitagliptin, (3) vorinostat, and (4) clonidine.

ABSTRACTA series of small molecules (SMs) containing fluorinated benzothiadiazole (BT) and phenothiazine (PT) were synthesized via the Suzuki coupling reaction. For exploring the effect of auxiliary electron-accepting moieties on the chemical structures, one or two fluorine atoms were selectively incorporated into the central electron-withdrawing BT unit of the non-fluorinated standard small molecule (BDP) to afford BDP-F and BDP-FF, respectively. Because of the favorable contributions of the fluorine atoms, the optimized inverted-type photovoltaic devices containing the SMs as active components exhibited improvement in PCE with increasing fluorine substitution, i.e., 0.49%, 0.54%, and 0.78% for BDP, BDP-F, and BDP-FF, respectively.

Transient Chemical Activation of Covalent Bonds for Healing of Kinetically Stable and Multifunctional Organohydrogels

To improve the understanding of passive film formation on metallic lithium in organic electrolyte, we synthesized and characterized lithium methyl carbonate (LiOCO{sub 2}CH{sub 3}), a prototypical component of the film. The chemical structure of this compound was characterized with Nuclear Magnetic Resonance (NMR) and Fourier Transform Infrared Spectroscopy (FTIR), and its thermal stability and decomposition pathway was studied by thermo-gravimetric analysis (TGA). The FTIR spectrum of chemically synthesized compound enabled us to resolve multiple products in the passive film on lithium in dimethyl carbonate (DMC). Lithium methyl carbonate is only one of the components, the others being lithium oxalate and lithium methoxide.

Three poly(arylene ether ketone)s (PAEKs) with propylidene (C1, C2) and phtalide (C3) fragments, and one phtalide-containing polyarylene (C4), were synthesized. Their chemical structures were confirmed via 1H NMR, 13C NMR and 19F NMR spectroscopy. The polymers have shown a high glass transition temperature (>155 °C), excellent film-forming properties, and a high free volume for this polymer type. The influence of various functional groups in the structure of PAEKs was evaluated. Expectedly, due to higher free volume the introduction of hexafluoropropylidene group to PAEK resulted in higher increase of gas permeability in comparison with propylidene group. The substitution of the fluorine-containing group on a rigid phtalide moiety (C3) significantly increases glass transition temperature of the polymer while gas permeation slightly decreases. Finally, the removal of two ether groups from PAEK structure (C4) leads to a rigid polymer chain that is characterized by highest free volume, gas permeability and diffusion coefficients among the PAEKs under investigation. Methods of modified atomic (MAC) and bond (BC) contributions were applied to estimate gas permeation and diffusion. Both techniques showed reasonable predicted parameters for three polymers while a significant underestimation of gas transport parameters was observed for C4. Gas solubility coefficients for PAEKs were forecasted by “Short polymer chain surface based pre-diction” (SPCSBP) method. Results for all three prediction methods were compared with the ex-perimental data obtained in this work. Predicted parameters were in good agreement with ex-perimental data for phtalide-containing polymers (C3 and C4) while for propylidene-containing poly(arylene ether ketone)s they were overestimated due to a possible influence of propylidene fragment on indices of oligomeric chains. MAC and BC methods demonstrated better prediction power than SPCSBP method.

X‐ray photoelectron spectroscopy (XPS), X‐ray absorption spectroscopy (XAS) and X‐ray Raman scattering (XRS) spectroscopy are the element‐specific tools providing local electronic and chemical structure insights. XPS is widely used for qualitative/quantitative surface analysis of solids and replaced by synchrotron‐based ambient pressure‐XPS and hard‐XPS to benefit from the advantages of near‐ambient pressures and hard X‐rays to study complex sample environments. XAS is a well‐established soft and hard X‐ray probe to examine the coordination, spin and oxidation states in solids, liquids and gases with high resolutions and short data acquisition time. XRS, using the hard X‐rays of ~10 keV, is preferred to conventional XAS and XPS in the study of soft X‐ray absorption edges such as C, O, Li K‐edges bulk‐sensitively in vacuum‐free medium for energy storage systems during working operations, inner Earth elements under realistic conditions, probing chemical and biological reactions in the liquid phase, C speciation in archeological and paleontological samples and direct tomography. In this study, first the basics of XPS, XAS, and XRS techniques with their advantages and limitations are introduced to provide a general overview. In the Results and Discussion, the particular emphasis is given to the comparison of XPS, XAS, and XRS with the interpretation of the spectroscopic signatures for organic carbonate‐based electrolytes. The computed C 1 s binding energy shifts for ethylene carbonate (EC), propylene carbonate (PC), dimethyl carbonate (DMC), and diethyl carbonate (DEC) are presented to discuss the XPS peak assignments and the performed C K‐edge XRS measurements of 1 M LiPF 6 in DMC and EC are given for a detailed discussion of the XRS spectra. C 1 s XPS, C K‐edge XAS, and XRS of PC from the literature are discussed to interpret the spectra of each technique comparatively.

Study on the decomposition mechanism of alkyl carbonate on lithium metal by pyrolysis-gas chromatography-mass spectroscopy

Globally, 8–13% of reproductive-age women experience PCOS, a complicated endocrine condition. This study investigates several methods for treating polycystic ovary syndrome (PCOS) as well as its causes, which include a complicated interplay between hereditary susceptibility, hormonal imbalances, insulin resistance, and lifestyle variables. An advanced graph-theory technique is used in the investigation of PCOS medication chemical structure prediction, with special focus on unique degree-based topological indices like the Banhatti and Zagreb indices. The efficacy of fifteen drugs, such as metformin, letrozole, spirolactone, etc., is evaluated in this study using QSPR analysis. We rate these medications using three different multi-criteria decision-making (MCDM) algorithms: CRITIC, CoCoSo, and MABAC. According to the results of the CoCoSo and MABAC analyses, the medicine orlistat has the best chance of success, and the MCDM methods have improved the process of evaluating and ranking treatment choices. The correlation between indices and properties falls within the range of 0.8–0.9, which shows a strong positive correlation with the physicochemical characteristics of compounds. The study demonstrates how topological indexes might improve the process of finding novel drugs and creating individualised treatment regimens for PCOS patients.

A novel and eco-friendly catalytic material, poly­(vinylalcohol)-supporteddeep eutectic solvent (PVA/DES) catalytic membrane, was prepared andused to produce value-added glycerin carbonate (GLC) from glycerinand dimethyl carbonate (DMC) for the first time. The heterogeneousform of catalytic membrane was prepared in the form of a film usingPVA polymer as a support and an environmentally friendly DES, consistingof potassium hydroxide (KOH) and poly­(ethylene glycol) (PEG), as thecatalyst. Fourier-transform infrared (FTIR) and thermogravimetricanalysis (TGA) characterizations were performed to assess the functionalgroups and thermal stability of the PVA/DES catalytic membrane. Moreover,the change in the chemical structure of the catalytic membrane afterthe reaction was investigated by FTIR. The interaction between thecatalytic membrane and glycerin is described by contact angle measurements.The relationship between reaction time, amount of catalyst, temperature,DMC/glycerin initial molar ratio, and reaction yield in batch reactionswith PVA/DES catalytic membrane was discussed. The maximum GLC yieldof 65.4% was obtained at 33% catalyst amount, 85 °C, DMC/glycerin:3:1, and a reaction time of 4 h. The additional membrane reactor studyincreased the 3 h GLC yield to 74.0%. Furthermore, the effects ofcatalyst amount and DMC/Glycerin ratio on the reaction yield in theResponse Surface Methodology (RSM) study with a central compositedesign were compared. It was found that the PVA/DES catalytic membraneis a suitable and green catalytic material for GLC synthesis fromDMC.

A typical traditional Chinese medicine (TCM) prescription (or formula) is composed of one or more kinds of herbs, which can play a certain role according to the appropriate dosage and production method. The number of possible TCM prescription is nearly as large as that of chemical structures, so the development of quantitative prescription-efficacy relationship models is as appealing as to build a quantitative structure-efficacy relationship model. In this paper, we first construct a binary network of prescriptions and herbs based on the real TCM data. Through the analysis of the network, we propose a method to calculate the expected value of herbs based on the node similarity, which represents the probability that two kinds of herbs exist in the same prescription. Moreover, we use the hierarchical clusters to classify the herb pairs whose expected value is greater than a certain value, and observe which herbs belong to the same category. In addition, we predict the efficacy of herbs by both classic K-means algorithm and improved K-means algorithm which is based on the node similarity. After verification, the results show that the improved algorithm can predict the efficacy of traditional Chinese medicine more accurately comparing with the classic K-means algorithm. Therefore the model can be used for predicting the potential efficacy of traditional Chinese medicine.

Machine learning (ML) models now play a crucial role in predicting properties essential to drug development, such as a drug's logscale acid-dissociation constant (pKa). Despite recent architectural advances, these models often generalize poorly to novel compounds due to a scarcity of ground-truth data. Further, these models lack interpretability. To this end, with deliberate molecular embeddings, atomic-resolution information is accessible in chemical structures by observing the model response to atomic perturbations of an input molecule. Here, we present BCL-XpKa, a deep neural network (DNN)-based multitask classifier for pKa prediction that encodes local atomic environments through Mol2D descriptors. BCL-XpKa outputs a discrete distribution for each molecule, which stores the pKa prediction and the model's uncertainty for that molecule. BCL-XpKa generalizes well to novel small molecules. BCL-XpKa performs competitively with modern ML pKa predictors, outperforms several models in generalization tasks, and accurately models the effects of common molecular modifications on a molecule's ionizability. We then leverage BCL-XpKa's granular descriptor set and distribution-centered output through atomic sensitivity analysis (ASA), which decomposes a molecule's predicted pKa value into its respective atomic contributions without model retraining. ASA reveals that BCL-XpKa has implicitly learned high-resolution information about molecular substructures. We further demonstrate ASA's utility in structure preparation for protein-ligand docking by identifying ionization sites in 93.2% and 87.8% of complex small molecule acids and bases. We then applied ASA with BCL-XpKa to identify and optimize the physicochemical liabilities of a recently published KRAS-degrading PROTAC.