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Topographic (3D) molecular connectivity indices based on molecular graphs weighted with quantum chemical parameters are used in QSPR and QSAR studies. These descriptors were compared to 2D connectivity indices (vertex and edge ones) and to quantum chemical descriptors in modeling partition coefficient (log P) and antibacterial activity of 2-furylethylene derivatives. In describing log P the 3D connectivity indices produced a significant improvement (more than 29%) in the predictive capacity of the model compared to those derived with topological and quantum chemical descriptors. The best linear discriminant model for classifying antibacterial activity of these compounds was also obtained with the use of 3D connectivity indices. The global percent of good classification obtained with 3D and 2D connectivity as well as quantum chemical descriptors were 94.1, 91.2, and 88.2, respectively. In general, all these models predict correctly the antibacterial activity of a set of nine new 2-furylethylene derivatives. The best result is obtained with 3D connectivity indices that classified correctly 100% of these compounds versus 88.9% obtained with 2D connectivity or quantum chemical descriptors.

A simple stochastic approach, designed to model the movement of electrons throughout chemical bonds, is introduced. This model makes use of a Markov matrix to codify useful structural information in QSAR. The self-return probabilities of this matrix throughout time ((SR)pi(k)) are then used as molecular descriptors. Firstly, a calculation of (SR)pi(k) is made for a large series of anticancer and non-anticancer chemicals. Then, k-Means Cluster Analysis allows us to split the data series into clusters and ensure a representative design of training and predicting series. Next, we develop a classification function through Linear Discriminant Analysis (LDA). This QSAR discriminates between anticancer compounds and non-active compounds with a correct global classification of 90.5% in the training series. The model also correctly classified 86.07% of the compounds in the predicting series. This classification function is then used to perform a virtual screening of a combinatorial library of coumarins. In this connection, the biological assay of some furocoumarins, selected by virtual screening using the present model, gives good results. In particular, a tetracyclic derivative of 5-methoxypsoralen (5-MOP) has an IC50 against HL-60 tumoral line around 6 to 10 times lower than those for 8-MOP and 5-MOP (reference drugs), respectively. Finally, application of Iso-contribution Zone Analysis (IZA) provides structural interpretation of the biological activity predicted with this QSAR.

Neurotoxicities of a series of solvents in rats and mice have been modeled by means of the TOPS-MODE approach. Two quantitative structure-toxicity relationship (QSTR) models were obtained explaining more than 80% of the variance in the experimental values of neurotoxicity of 45 solvents. Only one compound was detected as statistical outlier for these models. In contrast, previous models explained less than 60% of the variance in this property for 44 solvents. Finally, the contributions to neurotoxicity in rats and mice for a series of structural fragments were found. Structural characteristics of chlorinated fragments responsible for their different neurotoxicities were analyzed. The differences in neurotoxic behavior of some fragments in rats and mice were also analyzed, which could give insights on the toxicological mechanism of action of solvents studied.