வெக்டர் பயாலஜி ஜர்னல்

Jean Pierre Doucet* and Annick Doucet-Panaye

Aedes aegypti mosquitoes are important vectors in the transmission of severe diseases responsible for million deaths per year. Intensive use of insecticides results in environmental damages and induced resistance in mosquitoes. Search for new molecules devoid of detrimental side effects is therefore an urgent need. In this context, we derived QSAR models for evaluating the acute toxicity of 74 carboxamides and related chemicals to females of Ae. aegypti. These models based on PaDEL, 2D topological descriptors or CODESSA, 2D/3D geometrical and quantum variables, involved multilinear regression (MLR), and various machine learning methods namely support vector machine (SVM), projection pursuit regression (PPR) and artificial neural network (ANN).

We considered first the full dataset, and then, a more homogeneous, reduced set of 50 compounds with non-conjugated carbonyl. In all cases, for data fitting and leave-one-out cross-validation, satisfactory results were attained. Good performance was also obtained for extended validation sets. Generally speaking, the modeling methods were broadly equivalent. PaDEL 2D descriptors worked better than 2D/3D CODESSA descriptors. A hybrid model combining the two descriptor sets gave improved results. Setting such QSAR models, linking activity to structural features of examined chemicals, will be of interest for prioritizing experimental tests on new candidates, and evaluate their toxicity and potential synergist effects.