Solubility Prediction of Drugs in Supercritical Carbon Dioxide Using Artificial Neural Network
Author(s):
Abstract:
The descriptors computed by HyperChem® software were employed to represent the solubility of 40 drug molecules in supercritical carbon dioxide using an artificial neural network with the architecture of 15-4-1. The accuracy of the proposed method was evaluated by computing average of absolute error (AE) of calculated and experimental logarithm of solubilities. The AE (±SD) of data sets was 0.4 (±0.3) when all data points were used as training set and the solubilities were back-calculated. The AE for predicted solubilities using a trained network employing 1/3 of data points from each set was 0.4 (±0.3) and this finding reveals that the network is well trained using a limited number of experimental data. To provide a full predictive method, data sets were divided into two sets and the network was trained using 20 data sets and the next 20 sets were used as prediction sets. The produced average AEs (±SD) were 1.7 (±1.1) and 1.6 (±1.5), for two sets of analyses. In these analyses, only the computational descriptors, temperature and pressure ofSC-CO2 were used and no experimental solubility data is employed.
Language:
English
Published:
Iranian Journal of Pharmaceutical Research, Volume:6 Issue: 4, Autumn 2007
Page:
243
magiran.com/p541868
دانلود و مطالعه متن این مقاله با یکی از روشهای زیر امکان پذیر است:
اشتراک شخصی
با عضویت و پرداخت آنلاین حق اشتراک یکساله به مبلغ 1,390,000ريال میتوانید 70 عنوان مطلب دانلود کنید!
اشتراک سازمانی
به کتابخانه دانشگاه یا محل کار خود پیشنهاد کنید تا اشتراک سازمانی این پایگاه را برای دسترسی نامحدود همه کاربران به متن مطالب تهیه نمایند!
توجه!
- حق عضویت دریافتی صرف حمایت از نشریات عضو و نگهداری، تکمیل و توسعه مگیران میشود.
- پرداخت حق اشتراک و دانلود مقالات اجازه بازنشر آن در سایر رسانههای چاپی و دیجیتال را به کاربر نمیدهد.
In order to view content subscription is required
Personal subscription
Subscribe magiran.com for 70 € euros via PayPal and download 70 articles during a year.
Organization subscription
Please contact us to subscribe your university or library for unlimited access!