- author = "Francesco Archetti and Stefano Lanzeni and Enza Messina and Leonardo Vanneschi",
- title = "Genetic programming for computational pharmacokinetics in drug discovery and development",
- journal = "Genetic Programming and Evolvable Machines",
- year = "2007",
- volume = "8",
- number = "4",
- pages = "413--432",
- month = dec,
- note = "special issue on medical applications of Genetic and Evolutionary Computation",
- keywords = "genetic algorithms, genetic programming, Computational pharmacokinetics, Drug discovery, QSAR, SMILES",
- ISSN = "1389-2576",
-
URL = "
https://rdcu.be/cYj4W",
-
DOI = "
10.1007/s10710-007-9040-z",
- size = "20 pages",
- abstract = "The success of a drug treatment is strongly correlated with the ability of a molecule to reach its target in the patient's organism without inducing toxic effects. Moreover the reduction of cost and time associated with drug discovery and development is becoming a crucial requirement for pharmaceutical industry. Therefore computational methods allowing reliable predictions of newly synthesised compounds properties are of outmost relevance. In this paper we discuss the role of genetic programming in predictive pharmacokinetics, considering the estimation of adsorption, distribution, metabolism, excretion and toxicity processes (ADMET) that a drug undergoes into the patient's organism. We compare genetic programming with other well known machine learning techniques according to their ability to predict oral bioavailability (%F), median oral lethal dose (LD50) and plasma-protein binding levels (%PPB). Since these parameters respectively characterise the percentage of initial drug dose that effectively reaches the systemic blood circulation, the harmful effects and the distribution into the organism of a drug, they are essential for the selection of potentially good molecules. Our results suggest that genetic programming is a valuable technique for predicting pharmacokinetics parameters, both from the point of view of the accuracy and of the generalisation ability.",
-
notes = "GP, LS2-GP, LS2-C-GP, DF-GP, AIC, Weka ANN, MLP, SVM,
Linear regression, PCFS, CorrFS
p429 'We have tried to investigate the reasons why GP ...'",
