Genetic Programming and Other Machine Learning Approaches to Predict Median Oral Lethal Dose (LD50) and Plasma Protein Binding Levels (%PPB) of Drugs

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@InProceedings{Archetti:2007:evobio,

• author = "Francesco Archetti and Stefano Lanzeni and Enza Messina and Leonardo Vanneschi",
• title = "Genetic Programming and Other Machine Learning Approaches to Predict Median Oral Lethal Dose (LD50) and Plasma Protein Binding Levels (%PPB) of Drugs",
• booktitle = "EvoBIO 2007, Proceedings of the 5th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics",
• year = "2007",
• editor = "Elena Marchiori and Jason H. Moore and Jagath C. Rajapakse",
• volume = "4447",
• series = "Lecture Notes in Computer Science",
• pages = "11--23",
• address = "Valencia, Spain",
• publisher_address = "Berlin Heidelberg NewYork",
• month = apr # " 11-13",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "0302-9743",
• ISBN = "3-540-71782-X",
• ISBN-13 = "978-3-540-71782-9",
• DOI = "doi:10.1007/978-3-540-71783-6_2",
• abstract = "Computational methods allowing reliable pharmacokinetics predictions for newly synthesised 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 characterise 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 generalisation ability.",
• notes = "EvoBIO2007",

}

Genetic Programming entries for Francesco Archetti Stefano Lanzeni Enza Messina Leonardo Vanneschi

Citations