title: Recent developments of in silico predictions of intestinal absorption and oral bioavailability.
abstract: Among the absorption, distribution, metabolism, elimination, and toxicity properties (ADMET), unfavorable oral bioavailability is indeed an important reason for stopping further development of the drug candidates. Thus, predictions of oral bioavailability and bioavailability-related properties, especially intestinal absorption are areas in need of progress to aid pharmaceutical drug development. In this article, we review recent developments in the prediction of passive intestinal absorption and oral bioavailability. The advances in the datasets used for model building, the molecular descriptors, the prediction models, and the statistical modeling techniques, are summarized. Furthermore, we compared the performance of one machine learning method, support vector machines (SVM), and one traditional classification method, recursive partitioning (RP), on the predictions of passive absorption. Our comparisons demonstrate that the complex machine learning method could give better predictions than the traditional approach. Finally we discuss the current challenges that remain to be addressed.
authors.affiliation: Functional Nano & Soft Materials Laboratory (FUNSOM), Soochow University, Suzhou 215123, P.R. China.
authors.forename: Tingjun
authors.initials: T
authors.lastname: Hou
authors.forename: Youyong
authors.initials: Y
authors.lastname: Li
authors.forename: Wei
authors.initials: W
authors.lastname: Zhang
authors.forename: Junmei
authors.initials: J
authors.lastname: Wang
mesh_terms: D000284:Administration, Oral; D000818:Animals; D001185:Artificial Intelligence; D001682:Biological Availability; D006801:Humans; D007408:Intestinal Absorption; D008954:Models, Biological; D004364:Pharmaceutical Preparations / Q000737:chemistry / Q000378:metabolism*; D021281:Quantitative Structure-Activity Relationship
chemical_list: D004364:Pharmaceutical Preparations