Computational methods allowing reliable pharmacokinetics predictions for newly synthesized compounds are critically relevant for drug discovery and development. Here we present an empirical study focusing on various versions of Genetic Programming and other well known Machine Learning techniques to predict Median Oral Lethal Dose (LD50) and Plasma Protein Binding (%PPB) levels. Since these two parameters respectively characterize the harmful effects and the distribution into human body of a drug, their accurate prediction is essential for the selection of effective molecules. The obtained results confirm that Genetic Programming is a promising technique for predicting pharmacokinetics parameters, both from the point of view of the accurateness and of the generalization ability. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Archetti, F., Lanzeni, S., Messina, E., & Vanneschi, L. (2007). Genetic programming and other machine learning approaches to predict Median Oral Lethal Dose (LD50) and Plasma Protein Binding Levels (%PPB) of drugs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4447 LNCS, pp. 11–23). Springer Verlag. https://doi.org/10.1007/978-3-540-71783-6_2
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