[Acute intravenous toxicity to mice calculations on the basis local regression models in superoverlapping clusters (LRMSC)].

Modeling of quantitative structure--activity relationships between physicochemical descriptors of organic chemicals and their acute intravenous toxicity in mice have been presented. This approach includes three steps: structure-similarity chemicals selection for every chemical-of-interest (clusterization); construction of quantitative structure--toxicity models for every cluster (without including of chemical-of-interest); application of obtained QSAR equations for chemical-of-interest toxicity estimation. This approach has been applied for acute intravenous toxicity calculations of 10241 organic chemicals. For 7759 chemicals which has enough quantity of structural neighbours with the Tanimoto index (Tc) on the level 0.30 and over, a standard deviation of calculation vs. experimental log(1/LD50) values is equal to 0.51 at the estimation of experimental determination on the level 0.50. The results of calculations isn't so good for remain chemicals (approximately 24%). It is connect with absence of sufficient number of structure similarity neighbours. It's assumed this QSAR approach can be useful for activity and toxicity prediction of chemicals large sets.

[Linear and nonlinear QSAR models of acute intravenous toxicity to mice for organic chemicals].

QSAR analysis of acute intravenous toxicity to mice for 68 monofunctional chemicals is presented. There compounds represents seven classes of organic chemicals: hydrocarbons (6 chemicals), alcohols (13), amides (22), amines (12), ethers (5), ketones (7), nitriles (3). Preliminary consideration of data for these chemicals showed that it is necessary to consider not only linear toxicity--descriptors relationships, but also nonlinear models. The linear and nonlinear QSAR models were considered for each from indicated classes of organic chemicals. Analogical models were constructed for whole subset of monofunctional chemicals. The statistical parameters and robustness of nonlinear models are essential better then statistics of linear models. Replacing a lipophilicity descriptor with molecular polarizability and H-bond ability in nonlinear models permits also to improve statistical characteristics. Clearly, if relationships between the intravenous toxicity of compounds bearing only a single functional group and lipophilicity are nonlinear, then similar relationships must be considered with compounds containing more than one functional group. To check up this idea whole set of small clusters containing structure relative compounds with few functional groups was examined from position of linear and nonlinear relationships between toxicity and lipophilicity. It was estimated in most causes advantages of nonlinear models.