Abstract
A series of 436 Munro database chemicals were studied with respect to their corresponding experimental LD50 values to investigate the possibility of establishing a global QSAR model for acute toxicity. Dragon molecular descriptors were used for the QSAR model development and genetic algorithms were used to select descriptors better correlated with toxicity data. Toxic values were discretized in a qualitative class on the basis of the Globally Harmonized Scheme: the 436 chemicals were divided into 3 classes based on their experimental LD50 values: highly toxic, intermediate toxic and low to non-toxic. The k-nearest neighbor (k-NN) classification method was calibrated on 25 molecular descriptors and gave a non-error rate (NER) equal to 0.66 and 0.57 for internal and external prediction sets, respectively. Even if the classification performances are not optimal, the subsequent analysis of the selected descriptors and their relationship with toxicity levels constitute a step towards the development of a global QSAR model for acute toxicity.
Highlights
The Munro database is comprised of 613 chemicals representing a variety of pharmaceuticals, agricultural and industrial chemicals, substances used in food production and chemicals that have an impact on the environment [1]
The descriptor selection based on the Genetic algorithms (GAs) strategy was applied to all 460 descriptors to build a k-nearest neighbor (k-NN)
The Munro database was used as the basis for model calibration, as it provides a general coverage of chemical space with respect to physico-chemical properties
Summary
The Munro database is comprised of 613 chemicals representing a variety of pharmaceuticals, agricultural and industrial chemicals, substances used in food production and chemicals that have an impact on the environment [1]. A range of computational approaches has previously been developed for classifying the Munro database chemicals. The authors proposed the approach as a method for establishing a toxicological threshold of concern (TTC) for all Munro database chemicals. The authors used a decision tree approach [2] to classify the selected Munro database chemicals into one of the three structural classes and reported that the cumulative distributions of No. Observe Effect Levels (NOELs) belonging to all chemicals varied considerably among all the three structural classes, which implied that “chemical structure defines toxicity”.
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