New Trends in Structure-Biodegradability Relationships

Problems related to structure-biodegradation models are discussed. They deal with the homogeneity of the data sets, the selection of an adequate statistical method, and the choice of the molecular descriptors. This short review shows that the Boolean backpropagation neural networks are promising tools to model biodegradation. To confirm this hypothesis, a heterogeneous learning set of 47 molecules and two testing sets of 23 and 17 chemicals weakly (0) or highly (1) biodegradable are described by means of 11 Boolean structural descriptors. A supervised neural network using the backpropagation algorithm generates 100% and 85% of good predictions for the learning and testing sets, respectively. The results obtained are compared with those obtained from classical regression analysis. Advantages of this new approach are given. A particular application of correspondence factor analysis is presented to transform the Boolean variables before their introduction in the neural network. Advantages of this statistical transformation are stressed.