title: Neural computational prediction of oral drug absorption based on CODES 2D descriptors.
abstract: A neural model based on a numerical molecular representation using CODES program to predict oral absorption of any structure is described. This model predicts both high and low-absorbed compounds with a global accuracy level of 74%. CODES/ANN methodology shows promising utilities not only as a conventional in silico tool in high-throughput screening or improvement of absorption capabilities procedures but also the improvement of in vitro-in vivo correlation could be addressed.
authors.affiliation: Instituto de Química Médica, CSIC, Juan de la Cierva, Madrid, Spain.
authors.forename: A
authors.initials: A
authors.lastname: Guerra
authors.forename: N E
authors.initials: NE
authors.lastname: Campillo
authors.forename: J A
authors.initials: JA
authors.lastname: Páez
mesh_terms: D000284:Administration, Oral; D006801:Humans; D008956:Models, Chemical; D016571:Neural Networks, Computer; D010539:Permeability; D021281:Quantitative Structure-Activity Relationship; D013678:Technology, Pharmaceutical