QSPR modelling of the octanol/water partition coefficient of organometallic substances by optimal SMILES-based descriptors

Abstract Usually, QSPR is not used to model organometallic compounds. We have modeled the octanol/water partition coefficient for organometallic compounds of Na, K, Ca, Cu, Fe, Zn, Ni, As, and Hg by optimal descriptors calculated with simplified molecular input line entry system (SMILES) notations. The best model is characterized by the following statistics: n=54, r2=0.9807, s=0.677, F=2636 (training set); n=26, r2=0.9693, s=0.969, F=759 (test set). Empirical criteria for the definition of the applicability domain for these models are discussed

Additive SMILES-Based Carcinogenicity Models: Probabilistic Principles in the Search for Robust Predictions

Optimal descriptors calculated with the simplified molecular input line entry system (SMILES) have been utilized in modeling of carcinogenicity as continuous values (logTD50). These descriptors can be calculated using correlation weights of SMILES attributes calculated by the Monte Carlo method. A considerable subset of these attributes includes rare attributes. The use of these rare attributes can lead to overtraining. One can avoid the influence of the rare attributes if their correlation weights are fixed to zero. A function, limS, has been defined to identify rare attributes. The limS defines the minimum number of occurrences in the set of structures of the training (subtraining) set, to accept attributes as usable. If an attribute is present less than limS, it is considered “rare”, and thus not used. Two systems of building up models were examined: 1. classic training-test system; 2. balance of correlations for the subtraining and calibration sets (together, they are the original training set: the function of the calibration set is imitation of a preliminary test set). Three random splits into subtraining, calibration, and test sets were analysed. Comparison of abovementioned systems has shown that balance of correlations gives more robust prediction of the carcinogenicity for all three splits (split 1: rtest2=0.7514, stest=0.684; split 2: rtest2=0.7998, stest=0.600; split 3: rtest2=0.7192, stest=0.728)

QSAR based on hybrid optimal descriptors as a tool to predict antibacterial activity against Staphylococcus aureus

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Improved molecular descriptors based on the optimization of correlation weights of local graph invariants

We report the calculation of boiling points for several alkyl alcohols through the use of improved molecular descriptors based on the optimization of correlation weights of local invariants of graphs. As local invariants we have used the presence of different chemical elements (i.e. C, H, and O) and the existence of different vertex degree values (i.e. 1, 2, 3 and 4). The inherent flexibility of the chosen molecular descriptor seems to be rather suitable to obtain satisfactory enough predictions of the property under study. Comparison with other similar approximation reveals a very good behavior of the present method. The use of higher order polynomials do not seem to be necessary to improve results regarding the simple linear fitting equations. Some possible future extensions are pointed out in order to achieve a more definitive conclusion about this approximation

CORAL: The dispersion of SWNTs in different organic solvents

Single-walled carbon nanotubes (SWNTs) are group of new substances with specific cylindrical architecture of their molecules. The dispersion of SWNTs in different organic solvents is parameter that can be valuable information for development of nanomaterials. The CORAL software is a tool to build up model for different endpoints using the Monte Carlo technique. In this work, the ability of the CORAL software to be a tool to predict dispersion of SWCTs in different organic solvents demonstrated

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)

Structure-toxicity relationships for aliphatic compounds based on Correlation Weighting of Local Graph Invariants

Quantitative Structure-Activity Relationships based on molecular descriptors calculated with Correlation Weights of Local Graph Invariants were developed to model the toxicity of aliphatic compounds to the 50% population growth inhibition. The relationships were computed on the basis of Labeled Hydrogen- Filled Graphs and correlation weights were obtained by an optimization to render as large as possible correlation coefficients between log(IGC 50-1) and descriptors calculated with correlation weights. Morgan extended connectivity indices of zero, first, and second orders, paths of lengths two and three and valence shells of second and third ranges have been tested as local invariants of the Labeled Hydrogen-Filled Graphs. The best quantitative relationship obtained from the optimization of correlation weights is that one based on the valence shell of range two. First, second, and third order fitting equations were determined and statistical results are better than other similar data for the same molecular set

Improved QSAR modeling of anti-HIV-1 acivities by means of the optimized correlation weights of local graph invariants

We report the results derived from the use of molecular descriptors calculated with the correlation weights (CWs) of local graph invariants for modeling of anti-HIV-1 potencies of two groups of reverse transcriptase inhibitors. The presence of different chemical elements in the molecular structure of the inhibitors and the Morgan extended connectivity values of zeroth-, first-, and second order have been examined as local graph invariants in the labeled hydrogen-filled graphs. We have computed via Monte Carlo optimization procedure the values of CWs which produce the largest possible correlation coefficient between the numerical data on the anti-HIV-1 potencies and those values of the descriptors on the training set. The model of the anti-HIV-1 activity obtained with compounds of training set by means of optimization of correlation weights of chemical elements present together with Morgan extended connectivity of first order makes up a sensible model for a satisfactory prediction of the endpoints of the compounds belonging to the test set.Facultad de Ciencias ExactasInstituto de Investigaciones Fisicoquímicas Teóricas y AplicadasCentro de Investigaciones del Medio Ambient

Maximum topological distances based indices as molecular descriptors for QSPR : 4. Modeling the enthalpy of formation of hydrocarbons from elements

The enthalpy of formation of a set of 60 hydroarbons is calculated on the basis of topological descriptors defined from the distance and detour matrices within the realm of the QSAR/QSPR theory. Linear and non-linear polynomials fittings are made and results show the need to resort to higher-order regression equations in order to get better concordances between theoretical results and experimental available data. Besides, topological indices computed from maximum order distances seems to yield rather satisfactory predictions of heats of formation for hydrocarbons.Facultad de Ciencias Exacta

Improved QSAR modeling of anti-HIV-1 acivities by means of the optimized correlation weights of local graph invariants

We report the results derived from the use of molecular descriptors calculated with the correlation weights (CWs) of local graph invariants for modeling of anti-HIV-1 potencies of two groups of reverse transcriptase inhibitors. The presence of different chemical elements in the molecular structure of the inhibitors and the Morgan extended connectivity values of zeroth-, first-, and second order have been examined as local graph invariants in the labeled hydrogen-filled graphs. We have computed via Monte Carlo optimization procedure the values of CWs which produce the largest possible correlation coefficient between the numerical data on the anti-HIV-1 potencies and those values of the descriptors on the training set. The model of the anti-HIV-1 activity obtained with compounds of training set by means of optimization of correlation weights of chemical elements present together with Morgan extended connectivity of first order makes up a sensible model for a satisfactory prediction of the endpoints of the compounds belonging to the test set.Facultad de Ciencias ExactasInstituto de Investigaciones Fisicoquímicas Teóricas y AplicadasCentro de Investigaciones del Medio Ambient