Computer Aided Aroma Design. I. Molecular knowledge framework

Computer Aided Aroma Design (CAAD) is likely to become a hot issue as the REACH EC document targets many aroma compounds to require substitution. The two crucial steps in CAMD are the generation of candidate molecules and the estimation of properties, which can be difficult when complex molecular structures like odours are sought and when their odour quality are definitely subjective whereas their odour intensity are partly subjective as stated in Rossitier’s review (1996). In part I, provided that classification rules like those presented in part II exist to assess the odour quality, the CAAD methodology presented proceeds with a multilevel approach matched by a versatile and novel molecular framework. It can distinguish the infinitesimal chemical structure differences, like in isomers, that are responsible for different odour quality and intensity. Besides, its chemical graph concepts are well suited for genetic algorithm sampling techniques used for an efficient screening of large molecules such as aroma. Finally, an input/output XML format based on the aggregation of CML and ThermoML enables to store the molecular classes but also any subjective or objective property values computed during the CAAD process

Računarski generisani molekulski deskriptori kao proxi-ji za modelovanje materijala i njihovog uticaja

For the prediction of material's properties and of interaction of molecules with the surroundings, one needs to know their properties. Usually, the molecular properties are revealed through experimental measurements. It can be a tedious, time-consuming, and costly work. On the other hand, computational chemistry readily generates a huge number of data which can provide various molecular descriptors. These can be various observable properties (bond lengths and angles, dipole moments, etc...), but also the unobservable properties (partial atomic charges, electronegativity, various latent variables ....). There is an urgent need to develop accurate and economical screening tools that predict potential toxicity and environmental burden of various chemicals. Equally important is the design of safer alternatives. Molecular modeling methods offer one of several complementary approaches to evaluate the risk to human health and the environment as a result of exposure to environmental chemicals. These tools can streamline the hazard assessment process by simulating possible modes of action and providing virtual screening tools that can help prioritize bioassay requirements. Tailoring these strategies to the particular challenges presented by environmental chemical interactions make them even more effective. Advances in the fields of computational chemistry and molecular toxicology in recent decades allow the development of predictive models that inform the design of molecules with reduced potential to be toxic to humans or to the environment. As an example we present the novel methodology for the computational evaluation of pKa values of various organic bases, based on calculation of partial atomic charges by a simple semiempirical QM method.Za predviđanje osobina materijala i njihove interakcije sa okolinom, treba poznavati njihove osobine. Obično se osobine molekula otkrivaju eksperimentalnim merenjima. To može biti mukotrpan dugotrajan i skup posao. Sa druge strane, računarska hemija lako daje veliki broj podataka koji mogu da obezbede različite deskriptore molekula. To mogu biti razne merljive veličine (dužine i uglovi veza, dipolni momenti, i sl...), ali i nemerljive osobine (parcijalna atomska naelektrisanja, elektronegativnost, razne latentne varijable ....). Postoji velika potreba za razvijanjem pouzdanih i ekonomičnih načina za skrininge kojima se predskazuje potencijalna otrovnost i opterećenje životne okoline raznim hemikalijama. Jednako važan je i dizajn bezbednijih alternativa. Metode molekulskog modelovanja nude jedan od nekoliko komplementarnih pristupa za procenu rizika za zdravlje ljudi i životne sredine kao posledicu izlaganja hemikalijama u okolišu. Ovim postupcima se može neprekidno vršiti procena opasnosti simuliranjem mogućih načina delovanja, a obezbeđivanje virtualnog skrininga može pomoći u određivanju prioriteta kod bio-eseja. Ukrajanjem ovih strategija u određene izazove interakcija hemikalija i životne sredine može iste učiniti efikasnijima. Napredak u računarskoj hemiji i molekulskoj toksikologiji postignut poslednjih decenija dozvoljava razvoj prediktivnih modela za racionalni dizajn molekula sa umanjenim potencijalom otrovnosti za ljude ili za životnu sredinu. Kao primer predstavljamo novu metodologiju za računarsko procenjivanje pKa vrednosti različitih organskih baza na osnovu izračunavanja parcijalnih atomskih naelektrisanja prostim semiempirijskim QM metodom

The Interplay between QSAR/QSPR Studies and Partial Order Ranking and Formal Concept Analyses

The often observed scarcity of physical-chemical and well as toxicological data hampers the assessment of potentially hazardous chemicals released to the environment. In such cases Quantitative Structure-Activity Relationships/Quantitative Structure-Property Relationships (QSAR/QSPR) constitute an obvious alternative for rapidly, effectively and inexpensively generatng missing experimental values. However, typically further treatment of the data appears necessary, e.g., to elucidate the possible relations between the single compounds as well as implications and associations between the various parameters used for the combined characterization of the compounds under investigation. In the present paper the application of QSAR/QSPR in combination with Partial Order Ranking (POR) methodologies will be reviewed and new aspects using Formal Concept Analysis (FCA) will be introduced. Where POR constitutes an attractive method for, e.g., prioritizing a series of chemical substances based on a simultaneous inclusion of a range of parameters, FCA gives important information on the implications associations between the parameters. The combined approach thus constitutes an attractive method to a preliminary assessment of the impact on environmental and human health by primary pollutants or possibly by a primary pollutant well as a possible suite of transformation subsequent products that may be both persistent in and bioaccumulating and toxic. The present review focus on the environmental – and human health impact by residuals of the rocket fuel 1,1-dimethylhydrazine (heptyl) and its transformation products as an illustrative example

QSPR Studies on Aqueous Solubilities of Drug-Like Compounds

A rapidly growing area of modern pharmaceutical research is the prediction of aqueous solubility of drug-sized compounds from their molecular structures. There exist many different reasons for considering this physico-chemical property as a key parameter: the design of novel entities with adequate aqueous solubility brings many advantages to preclinical and clinical research and development, allowing improvement of the Absorption, Distribution, Metabolization, and Elimination/Toxicity profile and “screenability” of drug candidates in High Throughput Screening techniques. This work compiles recent QSPR linear models established by our research group devoted to the quantification of aqueous solubilities and their comparison to previous research on the topic

In Silico Prediction of Physicochemical Properties

This report provides a critical review of computational models, and in particular(quantitative) structure-property relationship (QSPR) models, that are available for the prediction of physicochemical properties. The emphasis of the review is on the usefulness of the models for the regulatory assessment of chemicals, particularly for the purposes of the new European legislation for the Registration, Evaluation, Authorisation and Restriction of CHemicals (REACH), which entered into force in the European Union (EU) on 1 June 2007. It is estimated that some 30,000 chemicals will need to be further assessed under REACH. Clearly, the cost of determining the toxicological and ecotoxicological effects, the distribution and fate of 30,000 chemicals would be enormous. However, the legislation makes it clear that testing need not be carried out if adequate data can be obtained through information exchange between manufacturers, from in vitro testing, and from in silico predictions. The effects of a chemical on a living organism or on its distribution in the environment is controlled by the physicochemical properties of the chemical. Important physicochemical properties in this respect are, for example, partition coefficient, aqueous solubility, vapour pressure and dissociation constant. Whilst all of these properties can be measured, it is much quicker and cheaper, and in many cases just as accurate, to calculate them by using dedicated software packages or by using (QSPRs). These in silico approaches are critically reviewed in this report.JRC.I.3-Toxicology and chemical substance

Quantitative Structure-Property Relationship Modeling & Computer-Aided Molecular Design: Improvements & Applications

The objective of this work was to develop an integrated capability to design molecules with desired properties. An automated robust genetic algorithm (GA) module has been developed to facilitate the rapid design of new molecules. The generated molecules were scored for the relevant thermophysical properties using non-linear quantitative structure-property relationship (QSPR) models. The descriptor reduction and model development for the QSPR models were implemented using evolutionary algorithms (EA) and artificial neural networks (ANNs). QSPR models for octanol-water partition coefficients (Kow), melting points (MP), normal boiling points (NBP), Gibbs energy of formation, universal quasi-chemical (UNIQUAC) model parameters, and infinite-dilution activity coefficients of cyclohexane and benzene in various organic solvents were developed in this work. To validate the current design methodology, new chemical penetration enhancers (CPEs) for transdermal insulin delivery and new solvents for extractive distillation of the cyclohexane + benzene system were designed. In general, the use of non-linear QSPR models developed in this work provided predictions better than or as good as existing literature models. In particular, the current models for NBP, Gibbs energy of formation, UNIQUAC model parameters, and infinite-dilution activity coefficients have lower errors on external test sets than the literature models. The current models for MP and Kow are comparable with the best models in the literature. The GA-based design framework implemented in this work successfully identified new CPEs for transdermal delivery of insulin, with permeability values comparable to the best CPEs in the literature. Also, new solvents for extractive distillation of cyclohexane/benzene with selectivities two to four times that of the existing solvents were identified. These two case studies validate the ability of the current design framework to identify new molecules with desired target properties.Chemical Engineerin

Development of a Predictive Equation of State for Equilibrium and Volumetric Properties of Diverse Molecules and Their Mixtures

Accurate prediction of the phase equilibrium and volumetric properties of pure fluids and their mixtures is essential for chemical process design and related applications. Although experiments provide accurate data at specific phase conditions, such data are limited and do not meet the ever-expanding industrial needs for process design and development. Therefore, a need exists for models that can provide accurate predictions of a wide range of thermodynamic properties. Cubic equations of state (CEOS) are widely used for calculations of thermodynamic properties; however, they often require experimental data for system-specific model tuning. An attractive alternative is to develop predictive equations of state that can estimate these properties based solely on the molecular structure - the most basic information that is generally available. In this work, the Peng-Robinson (PR) EOS is the focus of such development.The two main objectives of this study are to (1) develop improved generalized models for critical properties, acentric factor and vapor-liquid equilibria (VLE) property predictions using a theory-framed quantitative structure-property relationship (QSPR) modeling approach and (2) develop a new volume-translation function with a scaling-law correction to predict liquid density for pure fluids and mixtures of diverse molecules.To facilitate model development, a comprehensive databases of experimental measurements was assembled for pure-fluid critical properties, acentric factors, and liquid densities as well as VLE and liquid densities of binary mixtures. QSPR models were then developed to provide a priori predictions for the critical properties, acentric factor and VLE properties. The newly developed QSPR models for the critical properties provided predictions within twice the experimental errors. Similarly for VLE predictions, the QSPR models resulted in approximately twice the errors obtained through the data regression analyses of the VLE systems considered. Also, a new volume-translation method for the PR EOS was developed. The volume-translation function parameter was generalized in terms of molecular properties of each fluid. Then, the volume-translated PR EOS was extended to predict liquid densities of diverse mixtures employing EOS conventional mixing rules. The volume-translation approach developed in this work has been shown capable of providing accurate predictions of liquid densities in the saturated as well as single-phase regions for pure fluids and mixtures over large ranges of pressure and temperature. Specifically, the new volume-translated PR EOS yielded errors that are three to six times lower than the corresponding predictions from the untranslated model.Chemical Engineerin

Quantitative structure-property relationship generalized activity coefficient models

Phase behavior properties of chemical species and their mixtures are essential to design chemical processes involving multiple phases. Thermodynamic models are used in phase equilibria calculations to determine properties, such as phase compositions and partition coefficients at specific temperatures and pressures. In the absence of experimental data, generalized models are employed to predict phase equilibria properties.The two main objectives of this study are to (1) develop improved generalized models for vapor-liquid equilibria (VLE) and liquid-liquid equilibria (LLE) property predictions using a theory-framed quantitative structure-property relationship (QSPR) modeling approach and (2) implement a new modification to the widely used nonrandom two-liquid (NRTL) activity coefficient model to reduce parameters correlation, which is a limitation of the original model.In this work, we assembled two databases consisting of 916 binary VLE and 342 binary low-temperature LLE data. Data regression analyses were performed to determine the interaction parameters of various activity coefficient models. Structural descriptors of the molecules were generated and used in developing QSPR models to estimate the regressed interaction parameters. The developed QSPR models for VLE systems provided phase equilibria property predictions within twice the errors obtained through the data regression analyses for VLE systems. For LLE systems, the QSPR models resulted in approximately three to four times the errors found from the regression analyses. Further, our methodology provides a priori and easily implementable QSPR models with a wider applicability range than that of the group-contribution model, UNIFAC.The newly modified model proposed in this work reduced the NRTL model to a one-parameter model and eliminated the parameter correlation. The original and modified NRTL models yield comparable accuracies in representing experimental equilibrium properties. The benefits of our modification include easy generalizability of the parameters, ability to classify VLE behaviors based on a single model parameter and fewer convergence problems in parameter regressions