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  • Neural computational prediction of oral drug absorption based on CODES 2D descriptors.

Neural computational prediction of oral drug absorption based on CODES 2D descriptors.

European journal of medicinal chemistry (2009-12-22)
A Guerra, N E Campillo, J A Páez
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.

MATERIALS
Product Number
Brand
Product Description

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Nicotinic acid, ≥99.5% (HPLC)
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Saccharin, ≥98%
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Chloroform solution, suitable for NMR (reference standard), 1% in acetone-d6 (99.9 atom % D)
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L-Ascorbic acid, puriss. p.a., ≥99.0% (RT)
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