- author = "Leonardo Vanneschi",
- title = "Applications of Genetic Programming in Drug Discovery and Pharmacokinetics",
- booktitle = "VI International Workshop on Nature Inspired Cooperative Strategies for Optimization (NICSO 2013)",
- year = "2013",
- editor = "German Terrazas and Fernando Esteban Barril Otero and Antonio D. Masegosa",
- volume = "512",
- series = "Studies in Computational Intelligence",
- pages = "x",
- address = "Canterbury, United Kingdom",
- month = sep # " 2-4",
- publisher = "Springer",
- note = "Plenary Talk",
- keywords = "genetic algorithms, genetic programming",
- isbn13 = "978-3-319-01691-7",
-
DOI = "
doi:10.1007/978-3-319-01692-4",
- size = "0.5 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 utmost relevance. In this talk, I discuss the role of Genetic Programming (GP) in predictive pharmacokinetics, considering the estimation of adsorption, distribution, metabolism, excretion and toxicity processes (ADMET) that a drug undergoes into the patient's organism. In particular, I discuss the ability of GP 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 effective molecules. In the last part of the talk, I show and discuss how recently defined geometric semantic genetic operators can dramatically affect the performances of GP for this kind of application, in particular on out-of-sample test data.",
-
notes = "Only abstract in proceedings. See also
\cite{Freitas:2013:NICSO}
http://www.nicso2013.org/programme.html
http://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-3-319-01691-7",
