A Genetic Algorithm for Detection of Relevant Descriptors in ADMET Prediction

CECCHINI, ROCÍO LUJÁN; SOTO, AXEL JUAN; VAZQUEZ, GUSTAVO ESTEBAN; PONZONI, IGNACIO

Angra dos Reis, Rio de Janeiro

Congreso; BSB 2007: Brazilian Symposium on Bioinformatics 2007; 2007

Departamento de Computación - Pontifícia Universidad Católica de Rio de Janeiro

In this work, a novel approach for descriptor selection aimed to physicochemical property prediction is presented. The capacity of determining the most significant set of descriptors is of great importance due to their contribution for improving ADMET prediction models. The proposed methodology combines a genetic algorithm with decision trees. Experimental analysis was carried out for predicting the octanol-water partition coefficient (logP) using neural networks as prediction method. The performance results showed the good potential of this technique.