Dynamic population variation in genetic programming
Information Sciences: an International Journal
Operator equalisation, bloat and overfitting: a study on human oral bioavailability prediction
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Using crossover based similarity measure to improve genetic programming generalization ability
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Genetic programming for QSAR investigation of docking energy
Applied Soft Computing
Using Operator Equalisation for Prediction of Drug Toxicity with Genetic Programming
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Genetic programming for anticancer therapeutic response prediction using the NCI-60 dataset
Computers and Operations Research
On the use of genetic programming for the prediction of survival in cancer
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Open issues in genetic programming
Genetic Programming and Evolvable Machines
Reassembling operator equalisation: a secret revealed
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Reassembling operator equalisation: a secret revealed
ACM SIGEVOlution
EvoBIO'10 Proceedings of the 8th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Random sampling technique for overfitting control in genetic programming
EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
Operator equalisation for bloat free genetic programming and a survey of bloat control methods
Genetic Programming and Evolvable Machines
Bloat free genetic programming: application to human oral bioavailability prediction
International Journal of Data Mining and Bioinformatics
Balancing learning and overfitting in genetic programming with interleaved sampling of training data
EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
A new implementation of geometric semantic GP and its application to problems in pharmacokinetics
EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
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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 synthesized 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 characterize 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 generalization ability.