Combination of artificial neural network technique and linear free energy relationship parameters in the prediction of gradient retention times in liquid chromatography-Reference-Cited by-同舟云学术

Combination of artificial neural network technique and linear free energy relationship parameters in the prediction of gradient retention times in liquid chromatography

Published:2008-05 Issue:1-2 Volume:1190 Page:241-252
ISSN:0021-9673
Container-title:Journal of Chromatography A
language:en
Short-container-title:Journal of Chromatography A

Author:

Fatemi M.H.,Abraham M.H.,Poole C.F.

Publisher

Elsevier BV

Subject

Organic Chemistry,General Medicine,Biochemistry,Analytical Chemistry

Reference78 articles.

1. Relationship between immobilised artificial membrane chromatographic retention and the brain penetration of structurally diverse drugs

2. Immobilized Artificial Membranes — screens for drug membrane interactions

3. Use of immobilized artificial membrane chromatography for drug transport applications††Abbreviations: blood-brain barrier, BBB; bovine brain microvessel endothelial cells, BBMEC; calculated log distribution coefficient (Pallas, CompuDrug, Budapest, Hungary), ClogD; calculated log partition coefficient, ClogP; capacity factor on an IAM column, k¢IAM; capacity factor on a reversed-phase column, k¢w; log distribution coefficient, log D; immobilized artificial membrane, IAM; nonsteroidal antiinflammatory, NSAID; octadecylsilicate, ODS; log octanol:water intrinsic partition coefficient, log P; phosphate-buffered saline, PBS; phosphatidylcholine, PC.

4. Chromatography and capillary electrophoresis in modelling the basic processes of drug action

Cited by 37 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Intelligent Recommendation Systems Powered by Consensus Neural Networks: The Ultimate Solution for Finding Suitable Chiral Chromatographic Systems?;Analytical Chemistry;2024-07-10

2. Advances in AI-Driven Retention Prediction for Different Chromatographic Techniques: Unraveling the Complexity;Critical Reviews in Analytical Chemistry;2023-09-06

3. Current trends in chromatographic prediction using artificial intelligence and machine learning;Analytical Methods;2023

4. Evaluation of Retention Range of Extractables Under Linear Gradient Conditions for Reversed-Phase Chromatographic Considerations and Requirements in Extractables Analytical Methods for Chemical Characterization of Medical Devices;Chromatographia;2022-07-07

5. Transfer of gas chromatographic retention data among poly(siloxane) columns by quantitative structure-retention relationships based on molecular descriptors of both solutes and stationary phases;Journal of Chromatography A;2022-01