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CODES/neural network model: A useful tool for in silico prediction of oral absorption and blood-brain barrier permeability of structurally diverse drugs

AuthorsDorronsoro Díaz, Isabel; Chana, A.; Abasolo, Ibane; Castro, Ana ; Gil, Carmen ; Stud Schlüter, Manfred; Martinez, A.
Issue Date2004
PublisherJohn Wiley & Sons
CitationQSAR and Combinatorial Science 23: 89- 98 (2004)
AbstractTwo different neural network models able to predict both oral absorption (OA) and blood-brain barrier (BBB) permeability of structurally diverse drugs in use clinically are presented here. Using the descriptors generated by CODES, a program which codifies molecules from a topological point of view, we avoid the uncertain choice of molecular conformation and physicochemical parameters. In this work, a method called Reduction of Dimensions, designed for compressing data, is applied for the first time in order to minimize the bias factor added to a QSAR study when the selection of descriptors are performed. A training set of 28 and 35 structurally diverse compounds are used for oral absorption and blood-brain barrier models respectively. The output data is quantitative in both cases and refers to percent of drug absorbed after oral administration (% Bioavailable values) for OA model and log (Cbrain/Cblood) (log BB) for BBB permeability model. The network training was completed and validated by the leave-one-out method (Prediction errors were 6.5% and 5.6% for OA and BBB permeability models respectively). Excellent correlations were obtained (r = 0.95, r = 0.94). Both models show good predictive abilities regarding to external validation on test sets.
Identifiersdoi: 10.1002/qsar.200330858
issn: 1611-020X
e-issn: 1611-0218
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