- 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
PMID20022146
RESUMEN
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.
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Cloroformo, HPLC Plus, for HPLC, GC, and residue analysis, ≥99.9%, contains amylenes as stabilizer
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Ácido L-ascórbico, suitable for cell culture, suitable for plant cell culture, ≥98%
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Glicina, from non-animal source, meets EP, JP, USP testing specifications, suitable for cell culture, ≥98.5%
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Cloroformo, HPLC Plus, for HPLC, GC, and residue analysis, ≥99.9%, contains 0.5-1.0% ethanol as stabilizer