While drawing a structure the toxicity risk predictor will start looking for potential toxicity risks as long as the currently drawn structure is a valid chemical entity. Toxicity risk alerts are an indication that the drawn structure may be harmful concerning the risk category specified. However, risk alerts are by nomeans meant to be a fully reliable toxicity prediction. Nor should be concluded from the absence of risk alerts that a particular substance is completely free of any toxic effect. In order to assess the toxicity prediction's reliability we ran a set of toxic compounds and a set of presumably non-toxic compounds through the prediction. The diagram below shows the results obtained by predicting allavailable structures of four subsets of the RTECS database. E.g. all structures known to be mutagenic were run through the mutagenicity assessment. 86 % of these structures where found to bear a high or medium risk of being mutagenic. As a controlset served a collection of traded drugs of which the mutagenicity risk assessment revealed only 12 % of potentially harmful compounds.
Theprediction process relies on a precomputed set of structural fragment that give rise to toxicity alerts in case they are encountered in the structure currently drawn. These fragment lists were created by rigorously shreddering all compounds of the RTECS database known to be active in a certain toxicity class (e.g. mutagenicity). During the shreddering any molecule was first cut at every rotatable bondsleading to a set of core fragments. These in turn were used to reconstruct all possible bigger fragments being a substructure of the original molecule. Afterwards, a substructure search process determined the occurence frequency of any fragment (core and constructed fragments) within all compounds of that toxicity class. It also determined these fragment's frequencies within the structures of morethan 3000 traded drugs. Based on the assumption that traded drugs are largely free of toxic effects, any fragment was considered a risk factor if it occured often as substructure of harmful compounds but never or rarely in traded drugs.
The logP value of a compound, which is the logarithm of its partition coefficient between noctanol and water log(coctanol/cwater), is a wellestablished measure of the compound's hydrophilicity. Low hydrophilicities and therefore high logP values cause poor absorption or permeation. It has been shown for compounds to have a reasonable propability of being well absorbt their logP value must not be greater than 5.0. The distribution of calculated logP values of more than 3000 drugs on the market underlines this fact (see diagram)
Ourin-house logP calculation method is implemented as increment system adding contributions of every atom based on its atom type. Alltogether the cLogP predicting engine distinguishes 368 atom types which are composed of various properties of the atom itself (atomic no and ring membership) as its direct neighbours (bond type, aromaticity state and encoded atomic no). More than 5000 compounds withexperimentally determined logP values were used as training set to optimize the 369 contribution values associated with the atom types. The correlation plot (see diagram) shows calculated versus experimentally determined logP values of an independent test set of more than 5000 compounds being different from the training set.
Octanol-water partition coefficient logP is used in QSAR studies andrational drug design as a measure of molecular hydrophobicity. Hydrophobicity affects drug absorption, bioavailability, hydrophobic drug-receptor interactions, metabolism of molecules, as well as their toxicity. LogP has become also a key parameter in studies of the environmental fate of chemicals. Method for logP prediction developed at Molinspiration (miLogP2.2 - November 2005) is based on group...