Physicochemical Properties Of Organic Compounds Research Articles

Use of partition coefficients in combination with the molecular formulas of solutes to predict physicochemical properties with improved accuracy

The partition coefficient (logP) between water and an organic solvent has been used widely to predict the physicochemical properties of organic compounds. However, many physicochemical properties cannot be predicted accurately with logP. In this study, a theoretical approach is used to investigate how the molecular sizes, hydrogen (H)-bond acceptors and donors of solutes affect the correlation between their physicochemical properties and logP. We find that the correlations, which are usually strong for nonpolar solutes, are weakened by the H-bond acceptors and/or donors of solutes. When the predictive variable, Sm, which can be easily calculated from the molecular formula of solutes, is included in the models for the prediction of physicochemical properties from logP, the statistical reliabilities and predictive power of the models increase remarkably. This strategy to improve the accuracy of the prediction of physicochemical properties using logP does not require additional experimental data or complicated calculations and can be easily used by researchers. It is expected that this strategy can find widespread application in accurately predicting the effects of organic compounds on human and environment.

Symmetry-Adapted Domination Indices: The Enhanced Domination Sigma Index and Its Applications in QSPR Studies of Octane and Its Isomers

Molecular descriptors are essential in mathematical chemistry for studying quantitative structure–property relationships (QSPRs), and topological indices are a valuable source of information about molecular properties, such as size, cyclicity, branching degree, and symmetry. Graph theory has played a crucial role in the development of topological indices and dominating parameters for molecular descriptors. A molecule graph, under graph isomorphism conditions, represents an invariant number, and the graph theory approach considers dominating sets, which are subsets of the vertex set where every vertex outside the set is adjacent to at least one vertex inside the set. The dominating sigma index, a topological index that incorporates the mathematical principles of domination topological indices and the sigma index, is applicable to some families of graphs, such as book graphs and windmill graphs, and some graph operations, which have exact values for this new index. To evaluate the effectiveness of the domination sigma index in QSPR studies, a comparative analysis was conducted to establish an appropriate domination index that correlates with the physicochemical properties of octane and its isomers. Linear and non-linear models were developed using the QSPR approach to predict the properties of interest, and the results show that both the domination forgotten and domination first Zagreb indices exhibited satisfactory performance in comparison testing. Further research into QSAR/QSPR domination indices is required to build more robust models for predicting the physicochemical properties of organic compounds while maintaining the importance of symmetry.

Density results for Graovac-Pisanski’s distance number

The sum of distances between every pair of vertices in a graph G is called the Wiener index of G. This graph invariant was initially utilized to predict certain physico-chemical properties of organic compounds. However, the Wiener index of G does not account for any of its symmetries, which are also known to effect these physico-chemical properties. Graovac and Pisanski modified the Wiener index of G to measure the average distance each vertex is displaced under the elements of the symmetry group of G; we call this the Graovac-Pisanski (GP) distance number of G. In this article, we prove that the set of all GP distance numbers of graphs with isomorphic symmetry groups is dense in a half-line. Moreover, for each finite group Γ and each rational number q within this half-line, we present a construction for a graph whose GP distance number is q and whose symmetry group is isomorphic to Γ. This construction results in graphs whose vertex orbits are not connected; we also consider an analogous construction which ensures that all vertex orbits are connected.

Permeability Data of Organosulfur Garlic Compounds Estimated by Immobilized Artificial Membrane Chromatography: Correlation Across Several Biological Barriers.

Among healthy vegetables, those of the genus Allium stand out. Antioxidant and anti-inflammatory properties have been associated with these vegetables, attributed mainly to organosulfur compounds (OSCs). In turn, they are linked to a protective effect counteracting cardiovascular disease development. Now, to really ensure the bioactive efficacy of the said compounds once consumed, it is necessary to previously evaluate the ADME (absorption, distribution, metabolism, and excretion) profile. Alternatively, in vitro and in silico methods attempt to avoid or reduce experimental animals’ use and provide preliminary information on drugs’ ability to overcome the various biological barriers inherent in the ADME process. In this sense, in silico methods serve to provide primary information on drugs’ bioavailability mechanisms. High-performance liquid chromatography (HPLC) using a stationary phase composed of phospholipids, the so-called immobilized artificial membrane (IAM), has been widely recognized as a valuable alternative method to extract and quantify information about the structure and physicochemical properties of organic compounds which are extensively used in studies of quantitative structure–activity relationships (QSARs). In the present study, the chromatographic capacity factors (log k’ (IAM)) for 28 OSCs were determined by IAM-HPLC. In order to evaluate the ability of the IAM phase in assessing lipophilicity of the compounds under study, several quantitative structure–retention relationships (QSRRs) were derived from exploring fundamental intermolecular interactions that govern the retention of compounds under study on IAM phases. As expected, the hydrophobic factors are of prime importance for the IAM retention of these compounds. However, the molecular flexibility and specific polar interactions expressed by several electronic descriptors (relative negative charge, RNCG, and Mulliken electronegativity) are also involved. We also evaluated the IAM phase ability to assess several ADME parameters for the OSCs under study obtained using the SwissADME web tool integrated into the SwissDrugDesign workspace and the PreADMET web tool. The human gastrointestinal absorption (HIA), blood–brain barrier (BBB) permeation, and skin permeability were investigated through QSAR modeling, using several chemometric approaches. The ADME properties under study are strongly dependent on hydrophobic factors as expressed by log k’(IAM), which provide evidence for the great potential of the IAM phases in the development of QSAR models.

Using Recurrent Dependences to Control, Adjust, and Recover Values of the Physicochemical Properties of Organic Compounds

Using Recurrent Dependences to Control, Adjust, and Recover Values of the Physicochemical Properties of Organic Compounds

On Szeged‐type indices of titanium oxideTiO2nanotubes

AbstractThe relations and correlation between the physicochemical characteristics of a chemical compounds and the molecular structure connectivity of the same compound are used in quantitative structure activity and property relationship studies (QSAR/QSPR) by using the appropriate graph‐theoretical techniques. Szeged index has been extensively studied for modeling physicochemical properties of organic compounds acting as drugs or possess pharmacological activity. In this paper, we consider the two‐dimensional lattices of titanium oxide nanotubes TiO2. We compute several Szeged‐type indices such as vertex Szeged, edge Szeged, edge‐vertex Szeged, total Szeged, Padmaker–Ivan, revised Szeged and revised‐edge Szeged indices of these graphs. We define new types of cuts that are different from orthogonal cuts and can be used to obtain Szeged‐type results of titania nanotubes.

Profiling and analysis of chemical compounds using pointwise mutual information

Pointwise mutual information (PMI) is a measure of association used in information theory. In this paper, PMI is used to characterize several publicly available databases (DrugBank, ChEMBL, PubChem and ZINC) in terms of association strength between compound structural features resulting in database PMI interrelation profiles. As structural features, substructure fragments obtained by coding individual compounds as MACCS, PubChemKey and ECFP fingerprints are used. The analysis of publicly available databases reveals, in accord with other studies, unusual properties of DrugBank compounds which further confirms the validity of PMI profiling approach. Z-standardized relative feature tightness (ZRFT), a PMI-derived measure that quantifies how well the given compound’s feature combinations fit these in a particular compound set, is applied for the analysis of compound synthetic accessibility (SA), as well as for the classification of compounds as easy (ES) and hard (HS) to synthesize. ZRFT value distributions are compared with these of SYBA and SAScore. The analysis of ZRFT values of structurally complex compounds in the SAVI database reveals oligopeptide structures that are mispredicted by SAScore as HS, while correctly predicted by ZRFT and SYBA as ES. Compared to SAScore, SYBA and random forest, ZRFT predictions are less accurate, though by a narrow margin (AccZRFT = 94.5%, AccSYBA = 98.8%, AccSAScore = 99.0%, AccRF = 97.3%). However, ZRFT ability to distinguish between ES and HS compounds is surprisingly high considering that while SYBA, SAScore and random forest are dedicated SA models, ZRFT is a generic measurement that merely quantifies the strength of interrelations between structural feature pairs. The results presented in the current work indicate that structural feature co-occurrence, quantified by PMI or ZRFT, contains a significant amount of information relevant to physico-chemical properties of organic compounds.

A general linear free energy relationship for predicting partition coefficients of neutral organic compounds

Predicting the effects of organic compounds on environments and biological systems is an important objective for environmental chemistry and human health. The logarithm (to base 10) of the n-octanoll–water partition coefficient has been widely used to predict the mentioned properties. However, the suitability of this parameter for the purpose has been questioned, since the environments relating to the properties may be quite different from that of bulk n-octanol. In this study, we used a theoretical derivation approach to develop a model for predicting the partition coefficients of solutes between water and an organic solvent that may be similar to n-octanol or quite different from it. Our model relies on solute descriptors that can be calculated based on solute structures. It was used to predict the n-octanoll–water, hexadecanel–water and chloroforml–water partition coefficients of solutes. The calculated values of the examined parameters agreed well with their experimental counterparts. The model can find application in the accurate prediction of the effects of organic compounds on environments and the physicochemical properties of organic compounds by a full in-silico approach and can provide useful guidance for improving some properties of organic compounds.

Examining plant uptake and translocation of emerging contaminants using machine learning: Implications to food security

When water and solutes enter the plant root through the epidermis, organic contaminants in solution either cross the root membranes and transport through the vascular pathways to the aerial tissues or accumulate in the plant roots. The accumulation of contaminants in plant roots and edible tissues is measured by root concentration factor (RCF) and fruit concentration factor (FCF). In this paper, 1) a neural network (NN) was applied to model RCF based on physicochemical properties of organic compounds, 2) correlation and significance of physicochemical properties were assessed using statistical analysis, 3) fuzzy logic was used to examine the simultaneous impacts of significant compound properties on RCF and FCF, 4) a clustering algorithm (k-means) was used to identify unique groups and discover hidden relationships within contaminants in various parts of the plants. The physicochemical cutoffs achieved by fuzzy logic for the RCF and the FCF were compared versus the cutoffs for compounds that crossed the plant root membranes and found their way into transpiration stream (measured by transpiration stream concentration factor, TSCF). The NN predicted the RCF with improved accuracy compared to mechanistic models. The analysis indicated that log Kow, molecular weight, and rotatable bonds are the most important properties for predicting the RCF. These significant compound properties are positively correlated with RCF while they are negatively correlated with TSCF. Comparing the relationships between compound properties in various plant tissues showed that compounds detected in the edible parts have physicochemical cutoffs that are more like the compounds crossing the plant root membranes (into xylem tissues) than the compounds accumulating in the plant roots, with clear relationships to food security. The cluster analysis placed the contaminants into three meaningful groups that were in agreement with the results of fuzzy logic.

Soil contamination by pesticides: molecular modeling of octanol / organic carbone partition coefficient

QSPR methods are often used to estimate the physicochemical properties of organic compounds and to predict their behavior in the environment. QSPR models were developed for the prediction of octanol/organic carbone partition coefficient (Koc) of an heterogeneous set of pesticides. The approaches based on multilinear regression (MLR), artificial neural networks (ANN), every time associated with genetic algorithm (GA) selection of the most important variables, lead to models of very different qualities. The modeling of octanol/organic carbone partition coefficient of a heterogeneous mixture of pesticides show that the various statistics for the sets of training and validation (multiple coefficients of determination and prediction; roots of squared errors averages) attest to the superiority of non-linear models (ANN) and their relevance.

Calculation of Wiener Indices of Thiazolides: The Potent Inhibitors of Hepatitis B Virus and Hepatitis C Virus Replication

Background: Hepatitis C Virus is the leading cause of death in liver-related diseases globally. The identification of innovative drug targets and inexpensive therapeutic agents remain a high priority for chronic HCV infection management. In the last few decades, the availability of highly effective interferon-free regimens for HCV treatment is the only possible option for cure of the large number of chronic HCV patients. Direct acting antiviral drugs, such as Sovaldi, are expensive and their efficacy varies from genotype to genotype. In addition, these drugs are advised in combination with ribavirin and interferon, which makes the treatment costly. Thiazolide derivatives are recently emerging antiviral agents that may change current and future therapies for liver complications caused by HBV and HCV. Nitazoxanides are synthetic nitrothiazolyl-salicylamide derivative of thiazolide, which have been used as anti-HCV drugs. The derivatives of thiazolides were randomly screened for anti-HCV and anti-HBV activity in a biological system, yet the stability and other properties of these compounds were not tested. Representing chemical compounds with Hosoya-polynomial produces closed forms of many topological indices, which correlate with the chemical properties of the material under investigation. These indices are used in the development of quantitative structure-activity relationships (QSARs), in which the biological activity and other properties of molecules like boiling point, stability, strain energy, radius of gyration, and viscosity are correlated with their structures.In this paper, Hosoya-polynomials and Weiner indices of some derivatives of thiazolides were determined. Graphs of Hosoya polynomial for these derivatives were also plotted. Objectives: In this study, therefore, it was attempted to extend the research to check the stability and other properties of thiazolide derivatives using the Hosoya-polynomial and Wiener index. Methods: In the first step, Hosoya polynomials of some famous classes of thiazolides were computed, followed by computation of F Wiener indices of thiazolide derivatives. Distance-based graph invariants were used as a structure-descriptor for predicting various physicochemical properties of organic compounds. The Weiner index of 8 derivatives of thiazolidines was computed, including RM-4832, RM-4848, RM-4850, RM-4851, RM-4852, RM-4850, Tizoxanide, and Nitazoxanide. Results: These indices indicate the physiochemical properties of these compounds in relation to stability in the given biological environment. The Wiener indices of the studied compounds ranged from 374 to 684. Out of 8 analyzed compounds, 3 showed Wiener indices of 374. RM-4863 had a maximum index value of 684. Graphs were also drawn for Hosoya polynomials of all these thiazole derivatives. Conclusions: The results confirmed that RM-4863 is a stable physiochemical compound, which might be used as an effective drug for treating viral infections, including HCV and HBV.

Exact Formula for Computing the Hyper-Wiener Index on Rows of Unit Cells of the Face-Centred Cubic Lattice

Abstract Similarly to Wiener index, hyper-Wiener index of a connected graph is a widely applied topological index measuring the compactness of the structure described by the given graph. Hyper-Wiener index is the sum of the distances plus the squares of distances between all unordered pairs of vertices of a graph. These indices are used for predicting physicochemical properties of organic compounds. In this paper, the graphs of lines of unit cells of the face-centred cubic lattice are investigated. The graphs of face-centred cubic lattice contain cube points and face centres. Using mathematical induction, closed formulae are obtained to calculate the sum of distances between pairs of cube points, between face centres and between cube points and face centres. The sum of these formulae gives the hyper-Wiener index of graphs representing face-centred cubic grid with unit cells connected in a row. In connection to integer sequences, a recurrence relation is presented based on binomial coefficients.

Does reference data on physicochemical properties of organic compounds need an additional processing?

Using normal boiling points of n-alkyl phosphines it is shown that some of available reference data on the physicochemical properties of organic compounds are unreliable or even erroneous. The use of such data for any practical purposes is undesirable. Among known methods for evaluating physicochemical characteristics of single compounds there are no ones providing a priori required precision. However, such demand can be fulfilled using the methods of comparison of properties of different series of homologues. The predestination of the quality control approach considered is refining physicochemical properties (A) of homologues. It implies their comparison (mutual recalculation) with analogous data for one or few series of analogues characterized by more reliable physicochemical data using three-parameter linear-logarithmic equation lg A(series I)= a lg A(series II) + bY + c, where A is the values of the property under consideration, Y is the additive parameter providing the mutually unambiguous correspondence of homologs of two series under comparison. In this paper the condition Y = nC (number of carbon atoms in the molecules of homologs under consideration) is selected. The following independent step of data processing is the controlling of correspondence of A-values to the recurrent relation A(n + 1) = aA(n) + b, where A(n + 1) is the A-value for homolog containing (n + 1) carbon atoms in the molecule, and A(n) is the A-value for previous homolog. Similar procedures of the quality control of reference data is recommended for different properties of organic compounds. Refs 21. Figs 1. Tables 1.

Technical Note: Development of chemoinformatic tools to enumerate functional groups in molecules for organic aerosol characterization

Abstract. Functional groups (FGs) can be used as a reduced representation of organic aerosol composition in both ambient and controlled chamber studies, as they retain a certain chemical specificity. Furthermore, FG composition has been informative for source apportionment, and various models based on a group contribution framework have been developed to calculate physicochemical properties of organic compounds. In this work, we provide a set of validated chemoinformatic patterns that correspond to (1) a complete set of functional groups that can entirely describe the molecules comprised in the α-pinene and 1,3,5-trimethylbenzene MCMv3.2 oxidation schemes, (2) FGs that are measurable by Fourier transform infrared spectroscopy (FTIR), (3) groups incorporated in the SIMPOL.1 vapor pressure estimation model, and (4) bonds necessary for the calculation of carbon oxidation state. We also provide example applications for this set of patterns. We compare available aerosol composition reported by chemical speciation measurements and FTIR for different emission sources, and calculate the FG contribution to the O : C ratio of simulated gas-phase composition generated from α-pinene photooxidation (using the MCMv3.2 oxidation scheme).

The Hyper-Wiener Index of Trees of Ordernwith Diameterd

The hyper-Wiener index is a kind of extension of the Wiener index, used for predicting physicochemical properties of organic compounds. The hyper-Wiener indexWW(G)is defined asWW(G)=1/2∑u,v∈VGdGu,v+dG2u,vwith the summation going over all pairs of vertices inG, anddGu,vdenotes the distance of the two verticesuandvin the graphG. In this paper, we obtain the second-minimum hyper-Wiener indices among all the trees withnvertices and diameterdand characterize the corresponding extremal graphs.

Like Degree Wiener Index for Molecular Graphs

A graph representing the molecular structure of a chemical compound is called a molecular graph. A molecular graph is a collection of points representing the atoms in the molecule and set of lines representing the covalent bonds. The physicochemical and biochemical properties of compounds depend strongly on their molecular structures. The construction and investigation of topological indices that could be used to describe molecular structures is one of the important directions of mathematical chemistry. The Wiener index of a graph is defined as the sum of distances between all pairs of vertices in a connected graph. Wiener index correlates well with many physicochemical properties of organic compounds and as such has been well studied over the last quarter of a century. In this paper we introduce a wiener type index called Like Degree Wiener index and study the same for few graph structures.

Surface-active substances in a laboratory simulated Titan׳s organic haze: Prebiotic microstructures

Titan, the largest satellite of Saturn, is a key planetary body for astrobiological studies due to its active organic chemistry, hydrocarbon lakes and possible subsurface water-ammonia liquids. We have investigated the physicochemical properties of organic compounds synthesized in a simulated Titan atmosphere. A laboratory analog of Titan׳s aerosols, called tholin, was produced by irradiation of a nitrogen/methane gas mixture. The primary aim was to determine whether tholin represent possible sources of surface-active substances that could have been involved in the formation of prebiotic structures. A tholin sample was extracted with chloroform-methanol and the chloroform soluble material was separated by two-dimensional thin layer chromatography. Fluorescence excited by UV light was used to identify the major components on the plates. After being scraped from the TLC plate, the components were eluted as specific fractions and investigated by surface chemical methods, FTIR, scanning electron microscopy and cyclic voltammetry. Fractions 1 and 2 were strongly fluorescent and surface active, producing films at air-water interfaces. When exposed to aqueous phases, components in fraction 1 form spherical microstructures resembling prebionts. The prebionts are precursor structures that might have evolved into the first living cells.

Unified Physicochemical Property Estimation Relationships (UPPER)

The knowledge of physicochemical properties of organic compounds becomes increasingly important in pharmaceutical sciences, chemical engineering, and other fields. In this study, we developed UPPER (Unified Physicochemical Property Estimation Relationships), a comprehensive model for the estimation of 20 physicochemical properties of organic compounds. UPPER is a system of thermodynamically sound relationships that relate the various phase-transition properties to one another, which includes transition heats, transition entropies, transition temperatures, molar volume, vapor pressure, solubilities and partition coefficients in different solvents, and so on. UPPER integrates group contributions with the molecular geometric factors that affect transition entropies. All of the predictions are directly based on molecular structure. As a result, the proposed model provides a simple and accurate prediction of the properties studied. UPPER is designed to predict industrially, pharmaceutically, and environmentally relevant physicochemical properties. It can be an aid for the efficient design and synthesis of compounds with optimal physicochemical properties.

Approximation of physicochemical properties of homologs using recurrent and related non‐recurrent relations

A number of updatings, both recurrent and non‐recurrent, can be suggested for recurrent relations A(n) = aA(n − 1) + b recently introduced into chemistry. Among them, the equation A(n) = a[A(n − 1) − A∞] + A∞, where A∞ denotes the limiting values of a physicochemical property at the number of carbon atoms in the molecule hypothetically tending to infinity, appears to be the most convenient and precise. The application of the new relations to evaluating the refractive indices, relative densities, and normal boiling points as examples is considered.The problem of evaluating the limiting values of various physicochemical properties, discussed in chemistry for a long time, should be reconsidered as going beyond pure theory. The concept becomes very important for real evaluation of properties of organic compounds, A(n).The application of recurrent relations simplifies the evaluation of the values of physicochemical properties of organic compounds. This problem seems to be the most important for low‐stability organic compounds, where only single homologs are properly characterized, whereas the experimentally determined properties for the most of them are lacking. Copyright © 2012 John Wiley & Sons, Ltd.

Computational Approaches to Estimating Octanol-Water Partition Coefficient and Water Solubility of Organic Compounds

Octanol-water partition coefficient and water solubility are two important physicochemical properties of organic compounds in medicinal chemistry and environmental chemistry studies.They are used as critical parameters in quantitative structure-activity relationship (QSAR) studies to model the adsorption, distribution and metabolism of drugs in the living body and the distribution of organic compounds in naturenvironment.The most widely applied approaches to calculating octanol-water partition coefficients and water solubility can be roughly divided into two categories:fragment-addition models and descriptor-based models.This article provides a comprehensive review on these approaches, discusses their strengths and shortcomings, and provides an outlook for possible future developments in this area.