- 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
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
Sigma-Aldrich
Chloroform, HPLC Plus, for HPLC, GC, and residue analysis, ≥99.9%, contains 0.5-1.0% ethanol as stabilizer
Sigma-Aldrich
Chloroform, ACS spectrophotometric grade, ≥99.8%, contains 0.5-1.0% ethanol as stabilizer
Sigma-Aldrich
Chloroform, contains ethanol as stabilizer, meets analytical specification of BP, 99-99.4% (GC)
Sigma-Aldrich
Glycine, puriss. p.a., reag. Ph. Eur., buffer substance, 99.7-101% (calc. to the dried substance)
Supelco
Chloroform solution, NMR reference standard, 1% in acetone-d6 (99.9 atom % D), NMR tube size 3 mm × 8 in.
Sigma-Aldrich
L-Ascorbic acid, puriss. p.a., ACS reagent, reag. ISO, Ph. Eur., 99.7-100.5% (oxidimetric)