Table of Contents

Abstract: n/a

Abstract: This study presents a review of biodegradability modeling efforts including a detailed assessment of two models developed using an artificial intelligence based methodology. Validation results for these models using an independent, quality reviewed database, demonstrate that the models perform well when compared to another commonly used biodegradability model, against the same data. The ability of models induced by an artificial intelligence methodology to accommodate complex interactions in detailed systems, and the demonstrated reliability of the approach evaluated by this study, indicate that the methodology may have application in broadening the scope of biodegradability models. Given adequate data for biodegradability of chemicals under environmental conditions, this may allow for the development of future models that include such things as surface interface impacts on biodegradability for example.

Abstract: Performance of the E-state descriptors was tested against simple counts of the 35 atom types that the Kier-Hall E-states are based upon, by building PLS models for clogP, aqueous solubility, human intestinal absorption (HIA) and blood brain barrier (BBB). The results indicate that the simple counts work at least as well as E-state descriptors in building models for solubility and BBB, while surprisingly, simple counts have outperformed E-states by 18% and 30%, respectively, when building the models for HIA and clogP.

Abstract: The interplay between ‘noise-deficient’ QSAR and Partial Order Ranking, including analysis of average linear ranks, constitutes an effective tool in giving substances which have not been investigated experimentally an identity by comparison with experimentally well-characterized, structurally similar compounds. It is disclosed that experimentally well-characterized compounds may serve as substitutes for highly toxic compounds in experimental studies without exhibiting the same extreme toxicity, while from an overall viewpoint they exhibit analogous environmental characteristics.

Abstract: We report the results of a calculation of the normal boiling points of a representative set of 200 organic molecules through the application of QSPR theory. For this purpose we have used a particular set of flexible molecular descriptors, the so called Correlation Weighting of Atomic Orbitals with Extended Connectivity of Zero- and First-Order Graphs of Atomic Orbitals. Although in general the results show suitable behavior to predict this physical chemistry property, the existence of some deviant behaviors points to a need to complement this index with some other sort of molecular descriptors. Some possible extensions of this study are discussed.

Abstract: On the basis of the inductive QSAR descriptors we have created a neural network-based solution enabling quantification of antibacterial activity in the series of 101 synthetic cationic polypeptides (CAMEL-s). The developed QSAR model allowed 80% correct categorical classification of antibacterial potencies of the CAMEL-s both in the training and the validation sets. The accuracy of the activity predictions demonstrates that a narrow set of 3D sensitive ‘inductive’ descriptors can adequately describe the aspects of intra- and intermolecular interactions that are relevant for antibacterial activity of the cationic polypeptides. The developed approach can be further expanded for the larger sets of biologically active peptides and can serve as a useful quantitative tool for rational antibiotic design and discovery.

Abstract: A comparative study of 36 molecular descriptors derived from the topologicaldistance matrix and van der Waals space is carried out within this paper. They arepartitioned into 16 generalized topological distance matrix indices, 11 topologicaldistance indices known in the literature (seven obtained from eigenvalues/eigenvectors ofdistance matrix), and 9 van der Waals molecular descriptors. The generalized topologicaldistance indices, ^kδ_λ (λ = 1 – 3, k = 1 – 4), are introduced in this work on the basis ofreciprocical distance matrix. Intercorrelation analysis reveals that topological distanceindices mostly contain the same type of information, while van der Waals indices can bebound to the shape or the size of molecules. Furthermore, we found that topologicaldistance indices are good for describing molecular size, and they may be viewed as bulkparameters. The most accurate QSPR models for predicting boiling point of alkanes arebased on some of the generalized, eigenvalues/eigenvectors topological distance indicesand the van der Waals descriptors of molecular size.

Abstract: A quantitative structure – antioxidant activity relationship (QSAR) study of 36 flavonoids was performed using the partial least squares projection of latent structures (PLS) method. The chemical structures of the flavonoids have been characterized by constitutional descriptors, two-dimensional topological and connectivity indices. Our PLS model gave a proper description and a suitable prediction of the antioxidant activities of a diverse set of flavonoids having clustering tendency.

Abstract: The connectivity index χ can be regarded as the sum of bond contributions. Inthis article, boiling point (bp)-oriented contributions for each kind of bond are obtainedby decomposing the connectivity indices into ten connectivity character bases and thendoing a linear regression between bps and the bases. From the comparison of bp-orientedcontributions with the contributions assigned by χ, it can be found that they are verysimilar in percentage, i.e. the relative importance of each particular kind of bond is nearlythe same in the two forms of combinations (one is obtained from the regression withboiling point, and the other is decided by the constructor of the χ index). This coincidenceshows an impersonality of χ on bond weighting and may provide us another interpretationof the efficiency of the connectivity index on many quantitative structure–activity/property relationship (QSAR or QSPR) results. However, we also found that χ’sweighting formula may not be appropriate for some other properties. In fact, there is nouniversal weighting formula appropriate for all properties/activities. Recomposition ofsome topological indices by adjusting the weights upon character bases according todifferent properties/activities is suggested. This idea of recomposition is applied to thefirst Zagreb group index M₁ and a large improvement has been achieved.

Abstract: In this paper we describe the application in QSPR/QSAR studies of a newgroup of molecular descriptors: atom, atom-type and total linear indices of the molecularpseudograph’s atom adjacency matrix. These novel molecular descriptors were used forthe prediction of boiling point and partition coefficient (log P), specific rate constant (logk), and antibacterial activity of 28 alkyl-alcohols and 34 derivatives of 2-furylethylenes,respectively. For this purpose two quantitative models were obtained to describe thealkyl-alcohols’ boiling points. The first one includes only two total linear indices andshowed a good behavior from a statistical point of view (R² = 0.984, s = 3.78, F = 748.57,q² = 0.981, and s_cv = 3.91). The second one includes four variables [3 global and 1 local(heteroatom) linear indices] and it showed an improvement in the description of physicalproperty (R² = 0.9934, s = 2.48, F = 871.96, q² = 0.990, and s_cv = 2.79). Later, linearmultiple regression analysis was also used to describe log P and log k of the 2-furyl-ethylenes derivatives. These models were statistically significant [(R² = 0.984, s = 0.143, and F = 113.38) and (R² = 0.973, s = 0.26 and F = 161.22), respectively] and showed very good stability to data variation in leave-one-out (LOO) cross-validation experiment [(q² = 0.93.8 and scv = 0.178) and (q² = 0.948 and s_cv = 0.33), respectively]. Finally, a linear discriminant model for classifying antibacterial activity of these compounds was also achieved with the use of the atom and atom-type linear indices. The global percent of good classification in training and external test set obtained was of 94.12% and 100.0%, respectively. The comparison with other approaches (connectivity indices, total and local spectral moments, quantum chemical descriptors, topographic indices and E- state/biomolecular encounter parameters) reveals a good behavior of our method. The approach described in this paper appears to be a very promising structural invariant, useful for QSPR/QSAR studies and computer-aided “rational” drug design.

Abstract: This report describes a new set of macromolecular descriptors of relevance toprotein QSAR/QSPR studies, protein’s quadratic indices. These descriptors are calculatedfrom the macromolecular pseudograph’s α-carbon atom adjacency matrix. A study of theprotein stability effects for a complete set of alanine substitutions in Arc repressorillustrates this approach. Quantitative Structure-Stability Relationship (QSSR) modelsallow discriminating between near wild-type stability and reduced-stability A-mutants. Alinear discriminant function gives rise to excellent discrimination between 85.4% (35/41)and 91.67% (11/12) of near wild-type stability/reduced stability mutants in training andtest series, respectively. The model’s overall predictability oscillates from 80.49 until82.93, when n varies from 2 to 10 in leave-n-out cross validation procedures. This valuestabilizes around 80.49% when n was

Abstract: We have used SOM and grid 3D and 4D QSAR schemes for modeling the activity of a series of dihydrofolate reductase inhibitors. Careful analysis of the performance and external predictivities proves that this method can provide an efficient inhibition model.

Abstract: mVolatile organic compounds (VOCs) play an important role in differentphotochemical processes in the troposphere. In order to predict their impact on ozoneformation processes a detailed knowledge about their abundance in the atmosphere as wellas their reaction rate constants is required. The QSPR models were developed for theprediction of reaction rate constants of volatile unsaturated hydrocarbons. The chemicalstructure was encoded by constitutional and topological indices. Multiple linear regressionmodels using CODESSA software was developed with the RMS_CV error of 0.119 log units.The chemical structure was encoded by six topological indices. Additionally, a regressionmodel using a variable connectivity index was developed. It provided worse cross-validation results with an RMS_CV error of 0.16 log units, but enabled a structuralinterpretation of the obtained model. We differentiated between three classes of carbonatoms: sp2-hybridized, non-allylic sp3-hybridized and allylic sp3-hybridized. The structuralinterpretation of the developed model shows that most probably the most importantmechanisms are the addition to multiple bonds and the hydrogen atom abstraction at allylicsites.

Abstract: We report on the calculation of normal boiling points for a series of n = 58 aliphatic alcohols using the variable connectivity index in which variables x and y are used to modify the weights on carbon (x) and oxygen atoms (y) in molecular graphs, respectively. The optimal regressions are found for x = 0.80 and y = -0.90. Comparison is made with available regressions on the same data reported previously in the literature. A refinement of the model was considered by introducing different weights for primary, secondary, tertiary, and quaternary carbon atoms. The standard error in the case of the normal boiling points of alcohols was slightly reduced with optimal weights for different carbon atoms from s = 4.1°C (when all carbon atoms were treated as alike) to s = 3.9 °C.

Abstract: A QSAR toxicity analysis has been performed for a series of 19 alkaloids with the lycoctonine skeleton. GA-MLRA (Genetic Algorithm combined with Multiple Linear Regression Analysis) technique was applied for the generation of two types of QSARs: first, models containing exclusively 3D-descriptors and second, models consisting of physicochemical descriptors. As expected, 3D-descriptor QSARs have better statistical fits. Physicochemical-descriptor containing models, that are in a good agreement with the mode of toxic action exerted by the alkaloids studied, have also been identified and discussed. In particular, TPSA (Topological Polar Surface Area) and nC=O (number of –C(O)– fragments) parameters give the best statistically significant mono- and bidescriptor models (when combined with lipophilicity, MlogP) confirming the importance of H-bonding capability of the alkaloids for binding at the receptor site.

Abstract: The variable Zagreb ^vM₂ index is introduced and applied to the structure-boiling point modeling of benzenoid hydrocarbons. The linear model obtained (thestandard error of estimate for the fit model S_fit=6.8 ^oC) is much better than thecorresponding model based on the original Zagreb M₂ index (S_fit=16.4 ^oC). Surprisingly,the model based on the variable vertex-connectivity index (S_fit=6.8 ^oC) is comparable tothe model based on ^vM₂ index. A comparative study with models based on the vertex-connectivity index, edge-connectivity index and several distance indices favours modelsbased on the variable Zagreb ^vM₂ index and variable vertex-connectivity index.However, the multivariate regression with two-, three- and four-descriptors givesimproved models, the best being the model with four-descriptors (but ^vM₂ index is notamong them) with S_fit=5 ^oC, though the four-descriptor model contaning ^vM₂ index isonly slightly inferior (S_fit=5.3 ^oC).

Abstract: Valence topological charge-transfer (CT) indices are applied to the calculationof dipole moments. The algebraic and vector semisum CT indices are defined. Thecombination of CT indices allows the estimation of the dipole moments. The model isgeneralized for molecules with heteroatoms. The ability of the indices for the descriptionof the molecular charge distribution is established by comparing them with the dipolemoments of homologous series of percutaneous enhancers (phenyl alcohols and4-alkylanilines). Linear and quadratic correlation models are obtained. CT indicesimprove the multivariable quadratic regression equations for the dipole moment. Thevariance decreases 97% (4-alkylanilines). No superposition of the corresponding G_k–J_kand G_k^V–J_k^V pairs is observed in the fits, which diminishes the risk of co-linearity. Theinclusion of the heteroatom in the π-electron system is beneficial for the description ofthe dipole moment, owing to either the role of the additional p orbitals provided by theheteroatom or the role of steric factors in the π-electron conjugation. Inclusion of aconjugated double bond in the alkyl chain lends to more rigid structures with dipolemoment variations lower than1%.