Genetic programming and other machine learning approaches to predict median oral Lethal Dose (LD50) and plasma protein binding levels (%PPB) of drugs

Authors:
Francesco Archetti;Stefano Lanzeni;Enza Messina;Leonardo Vanneschi
Affiliations:
D.I.S.Co., Department of Computer Science and Communication, University of Milan-Bicocca, Milan, Italy and Consorzio Milano Ricerche, Milan, Italy;D.I.S.Co., Department of Computer Science and Communication, University of Milan-Bicocca, Milan, Italy;D.I.S.Co., Department of Computer Science and Communication, University of Milan-Bicocca, Milan, Italy;D.I.S.Co., Department of Computer Science and Communication, University of Milan-Bicocca, Milan, Italy
Venue:
EvoBIO'07 Proceedings of the 5th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
Year:
2007

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Abstract

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.