Usporedba QSPR modela zasnovanih na vodikom-popunjenim molekularnim grafovima i na grafovima atomskih orbitala

QSPR models are studied for normal boiling points of alkanes, alkylbenzenes, and polyaromatic hydrocarbons, in terms of optimized correlation weights of local invariants of the hydrogen- -filled graphs (HFGs) and of the graphs of atomic orbitals (GAOs). Morgan extended connectivities of the zeroth, first, and second order of the HFGs and GAOs were employed. The best QSPR model obtained is based on optimized correlation weights of the extended connectivity of the first order of the GAO. The statistical characteristics of this model are: n = 70, r superscript(2) = 0.9988, s = 5.8 °C, F = 57437 (training set); n = 70, r superscript(2) = 0.9985, s = 6.7 °C, F = 45154 (test set).Istraživani su QSPR modeli za normalnu točku vrelišta alkana, alkilbenzena i poliaromatskih ugljikovodika, zasnovani na optimiziranim korelacijskim te`inama lokalnih invarijanti vodikom-popunjenih molekularnih grafova (HFG) i grafova atomskih orbitala (GAO). Primjenjeni su Morganovi indeksi proširene povezanosti nultoga, prvoga i drugoga reda, kako za HFG tako i za GAO. Najbolji QSPR model je dobiven na osnovi optimiziranih korelacijskih težina za proširenu povezanost prvoga reda za GAO. Statističke karakteristike ovoga modela su: n = 70, r superscript(2) = 0.9988, s = 5.8 °C, F = 57437 (training set); n = 70, r superscript(2) = 0.9985, s = 6.7 °C, F = 45154 (test set)

QSAR Model for Cytotoxicity of Silica Nanoparticles on Human Embryonic Kidney Cells1

Abstract A predictive model for cytotoxicity of 20 and 50 nm silica nanoparticles has been built using so-called optimal descriptors as mathematical functions of size, concentration and exposure time. These parameters have been encoded into 31 combinations 'concentration-exposure-size'. The calculation has been carried out by means of the CORAL software ( http://www.insilico.eu/coral/ ) using three random splits of the obtained systems into training and test sets. The statistical quality of the best model for cell viability (%) of cultured human embryonic kidney cells (HEK293) exposed to different concentrations of silica nanoparticles measured by MTT assay is satisfactory

Advancement of predictive modeling of zeta potentials (ζ) in metal oxide nanoparticles with correlation intensity index (CII)

It was expected that index of the ideality of correlation (IIC) and correlation intensity index (CII) could be used as possible tools to improve the predictive power of the quantitative model for zeta potential of nanoparticles. In this paper, we test how the statistical quality of quantitative structure-activity models for zeta potentials (ζ, a common measurement that reflects surface charge and stability of nanomaterial) could be improved with the use of these two indexes. Our hypothesis was tested using the benchmark data set that consists of 87 measurements of zeta potentials in water. We used quasi-SMILES molecular representation to take into consideration the size of nanoparticles in water and calculated optimal descriptors and predictive models based on the Monte Carlo method. We observed that the models developed with utilization of CII are statistically more reliable than models obtained with the IIC. However, the described approach gives an improvement of the statistical quality of these models for the external validation sets to the detriment for the training sets. Nevertheless, this circumstance is rather an advantage than a disadvantage

QSPR modeling for enthalpies of formation of organometallic compounds by means of SMILES-based optimal descriptors

Organometallic compounds are an important class of chemicals, because many of them have vital biochemical features. There are studies on the quantitative structure-property/activity relationships (QSPR/QSAR) for organic substances [1-5], but only a few articles have deal with QSPR for organometallic compounds Simplified molecular input line entry system (SMILES) [9-13] has been used as an alternative for molecular graphs in the QSPR/QSAR analyses SMILES-based optimal descriptors were calculated a

QSPR modelling of normal boiling points and octanol/water partition coefficient for acyclic and cyclic hydrocarbons using SMILES-based optimal descriptors

AbstractPredictive quantitative structure - property relationships (QSPR) have been established for normal boiling points and octanol/water partition coefficient for acyclic and cyclic hydrocarbons using optimal descriptors calculated with simplified molecular input line entry system (SMILES). The probabilistic criteria for a rational definition of the domain of applicability of these models are discussed

CORAL: the prediction of biodegradation of organic compounds with optimal SMILES-based descriptors

Abstract CORAL software (http:/www.insilico.eu/coral) has been used to build up quantitative structure-biodegradation relationships (QSPR). The normalized degradation percentage has been used as the measure of biodegradation (for diverse organic compounds, n=445). Six random splits into sub-training, calibration, and test sets were examined. For each split the QSPR one-variable linear regression model based on the SMILES-based optimal descriptors has been built up. The average values of numbers of compounds and the correlation coefficients (r2) between experimental and calculated biodegradability values of these six models for the test sets are n=88.2±11.7 and r2=0.728±0.05. These six models were further tested against a set of chemicals (n=285) for which only categorical values (biodegradable or not) were available. Thus we also evaluated the use of the model as a classifier. The average values of the sensitivity, specificity, and accuracy were 0.811±0.019, 0.795±0.024, and 0.803±0.008, respectively

Nano-QSAR: Model of mutagenicity of fullerene as a mathematical function of different conditions

The experimental data on the bacterial reverse mutation test (under various conditions) on C60 nanoparticles for the cases (i) TA100, and (ii) WP2uvrA/pkM101 are examined as endpoints. By means of the optimal descriptors calculated with the Monte Carlo method a mathematical model of these endpoints has been built up. The models are a mathematical function of eclectic data such as (i) dose (g/plate); (ii) metabolic activation (i.e. with mix S9 or without mix S9); and (iii) illumination (i.e. darkness or irradiation). The eclectic data on different conditions were represented by so-called quasi-SMILES. In contrast to the traditional SMILES which are representation of molecular structure, the quasi-SMILES are representation of conditions by sequence of symbols. The calculations were carried out with the CORAL software, available on the Internet at http://www.insilico.eu/coral. The main idea of the suggested descriptors is the accumulation of all available eclectic information in the role of logical and digital basis for building up a model. The computational experiments have shown that the described approach can be a tool to build up models of mutagenicity of fullerene under different conditions

development of qsar models for predicting anti hiv 1 activity using the monte carlo method

AbstractAbstract The CORAL software (http://www.insilico.eu/coral/) has been examined as a tool for modeling anti-HIV-1 activity by quantitative structure — activity relationships (QSAR) for three different sets: (i) TIBO derivatives (n=82) (ii) anti-HIV-1 activity of 2-amino-6-arylsulfonylbenzonitriles and their congeners (n=64), and (iii) the measured binding affinity for fullerene-based HIV-1 PR inhibitors (n=48). A new global invariant ATOMPAIR of the molecular structure which can be calculated with the simplified molecular input line entry system (SMILES) was studied. The ATOMPAIR is an indicator of the joint presence of pairs of chemical elements (F, Cl, Br, N, O, S, and P) and three types of bonds (double covalent bond, triple covalent bond, and stereo chemical bond). Six random splits into sub-training, calibration, and test set were examined for each set. For the three aforementioned sets, the use of ATOMPAIR in the modeling process improves the predictive potential of the models for six random splits. Graphical abstrac

QSPR modeling of Gibbs free energy of organic compounds by weighting of nearest neighboring codes

We examine the encoding of chemical structure of organic compounds by Labeled Hydrogen-Filled Graphs (LHFGs). Quantitative Structure-Property Relationships (QSPR) for a representative set of 150 organic molecules have been derived by means of the optimization of correlation weights of local invariants of the LHFGs. We have tested as local invariants Morgan extended connectivity of zero- and first order, numbers of path of length 2 (P2) and valence shells of distance of 2 (S2) associated with each atom in the molecular structure, and the Nearest Neighboring Codes (NNC). The best statistical characteristics for the Gibbs free energy has been obtained for the NNC weighting. Statistical parameters corresponding to this model are the following n = 100, r2 = 0.9974, s = 5.136 kJ/mol, F = 38319 (training set); n = 50, r2 = 0.9990, s = 3.405 kJ/mol, F = 48717 (test set). Some possible further developments are pointed out.Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicada

Modeling of dendrite growth from undercooled nickel melt: sharp interface model versus enthalpy method

The dendritic growth of pure materials in undercooled melts is critical to understanding the fundamentals of solidification. This work investigates two new insights, the first is an advanced definition for the two-dimensional stability criterion of dendritic growth and the second is the viability of the enthalpy method as a numerical model. In both cases, the aim is to accurately predict dendritic growth behavior over a wide range of undercooling. An adaptive cell size method is introduced into the enthalpy method to mitigate against `narrow-band features' that can introduce significant error. By using this technique an excellent agreement is found between the enthalpy method and the analytic theory for solidification of pure nickel