TY - JOUR
T1 - Recent developments of in silico predictions of intestinal absorption and oral bioavailability
AU - Hou, Tingjun
AU - Li, Youyong
AU - Zhang, Wei
AU - Wang, Junmei
N1 - Copyright:
Copyright 2009 Elsevier B.V., All rights reserved.
PY - 2009/6
Y1 - 2009/6
N2 - 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.
AB - 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.
KW - ADMET
KW - Bioavailability
KW - In silico prediction
KW - Intestinal absorption
KW - Machine learning
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U2 - 10.2174/138620709788489082
DO - 10.2174/138620709788489082
M3 - Review article
C2 - 19519329
AN - SCOPUS:66849113847
SN - 1386-2073
VL - 12
SP - 497
EP - 506
JO - Combinatorial Chemistry and High Throughput Screening
JF - Combinatorial Chemistry and High Throughput Screening
IS - 5
ER -