Literature DB >> 12047885

Predicting ADME properties in silico: methods and models.

Darko Butina1, Matthew D Segall, Katrina Frankcombe.   

Abstract

Unfavourable absorption, distribution, metabolism and elimination (ADME) properties have been identified as a major cause of failure for candidate molecules in drug development. Consequently, there is increasing interest in the early prediction of ADME properties, with the objective of increasing the success rate of compounds reaching development. This review explores in silico approaches and selected published models for predicting ADME properties from chemical structure alone. In particular, we provide a comparison of methods based on pattern recognition to identify correlations between molecular descriptors and ADME properties, structural models based on classical molecular mechanics and quantum mechanical techniques for modelling chemical reactions.

Mesh:

Year:  2002        PMID: 12047885     DOI: 10.1016/s1359-6446(02)02288-2

Source DB:  PubMed          Journal:  Drug Discov Today        ISSN: 1359-6446            Impact factor:   7.851


  32 in total

Review 1.  Modeling kinetics of subcellular disposition of chemicals.

Authors:  Stefan Balaz
Journal:  Chem Rev       Date:  2009-05       Impact factor: 60.622

Review 2.  Towards quantitative prediction of oral drug absorption.

Authors:  Jennifer B Dressman; Kirstin Thelen; Ekarat Jantratid
Journal:  Clin Pharmacokinet       Date:  2008       Impact factor: 6.447

3.  In silico modeling of non-linear drug absorption for the P-gp substrate talinolol and of consequences for the resulting pharmacodynamic effect.

Authors:  Marija Tubic; Daniel Wagner; Hilde Spahn-Langguth; Michael B Bolger; Peter Langguth
Journal:  Pharm Res       Date:  2006-08       Impact factor: 4.200

4.  Enone- and chalcone-chloroquinoline hybrid analogues: in silico guided design, synthesis, antiplasmodial activity, in vitro metabolism, and mechanistic studies.

Authors:  Eric M Guantai; Kanyile Ncokazi; Timothy J Egan; Jiri Gut; Philip J Rosenthal; Ravi Bhampidipati; Anitha Kopinathan; Peter J Smith; Kelly Chibale
Journal:  J Med Chem       Date:  2011-05-05       Impact factor: 7.446

5.  PBPK Modeling to Simulate the Fate of Compounds in Living Organisms.

Authors:  Frédéric Y Bois; Cleo Tebby; Céline Brochot
Journal:  Methods Mol Biol       Date:  2022

6.  Gram-Scale Synthesis of 1,8-Naphthyridines in Water: The Friedlander Reaction Revisited.

Authors:  Shubhranshu Shekhar Choudhury; Subhrakant Jena; Dipak Kumar Sahoo; Shamasoddin Shekh; Rajiv K Kar; Ambuj Dhakad; Konkallu Hanumae Gowd; Himansu S Biswal
Journal:  ACS Omega       Date:  2021-07-12

7.  Protein-ligand interaction prediction: an improved chemogenomics approach.

Authors:  Laurent Jacob; Jean-Philippe Vert
Journal:  Bioinformatics       Date:  2008-08-01       Impact factor: 6.937

8.  Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework.

Authors:  Yoshihiro Yamanishi; Masaaki Kotera; Minoru Kanehisa; Susumu Goto
Journal:  Bioinformatics       Date:  2010-06-15       Impact factor: 6.937

Review 9.  Machine learning approaches and databases for prediction of drug-target interaction: a survey paper.

Authors:  Maryam Bagherian; Elyas Sabeti; Kai Wang; Maureen A Sartor; Zaneta Nikolovska-Coleska; Kayvan Najarian
Journal:  Brief Bioinform       Date:  2021-01-18       Impact factor: 11.622

10.  QSAR, molecular docking and ADMET properties in silico studies of novel 4,5,6,7-tetrahydrobenzo[D]-thiazol-2-Yl derivatives derived from dimedone as potent anti-tumor agents through inhibition of C-Met receptor tyrosine kinase.

Authors:  Ossama Daoui; Souad Elkhattabi; Samir Chtita; Rachida Elkhalabi; Hsaine Zgou; Adil Touimi Benjelloun
Journal:  Heliyon       Date:  2021-07-03
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