Troubleshooting computational methods in drug discovery

作者: Sandhya Kortagere , Sean Ekins

DOI: 10.1016/J.VASCN.2010.02.005

关键词: TroubleshootingWorkflowAdme toxADMEQuantitative structure–activity relationshipMachine learningDrug discoveryCheminformaticsBiologyArtificial intelligenceBioinformaticsToxicologyPharmacology

摘要: Computational approaches for drug discovery such as ligand-based and structure-based methods, are increasingly seen an efficient approach lead well providing insights on absorption, distribution, metabolism, excretion toxicity (ADME/Tox). What is perhaps less known widely described the limitations of different technologies. We have therefore presented a troubleshooting to QSAR, homology modeling, docking hybrid methods. If computational or cheminformatics methods become more used by non-experts it critical that brought user's attention addressed during their workflows. This could improve quality models results obtained.

参考文章(138)
Chris Williams, Suzanne Schreyer, Reverse fingerprinting and mutual information-based activity labeling and scoring (MIBALS). Combinatorial Chemistry & High Throughput Screening. ,vol. 12, pp. 424- 439 ,(2009) , 10.2174/138620709788167953
Santosh Khedkar, Alpeshkumar Malde, Evans Coutinho, Sudha Srivastava, Pharmacophore modeling in drug discovery and development: an overview. Medicinal Chemistry. ,vol. 3, pp. 187- 197 ,(2007) , 10.2174/157340607780059521
Robert P. Sheridan, Bradley P. Feuston, Vladimir N. Maiorov, Simon K. Kearsley, Similarity to molecules in the training set is a good discriminator for prediction accuracy in QSAR. Journal of Chemical Information and Computer Sciences. ,vol. 44, pp. 1912- 1928 ,(2004) , 10.1021/CI049782W
Sandhya Kortagere, Dmitriy Chekmarev, William J. Welsh, Sean Ekins, New Predictive Models for Blood—Brain Barrier Permeability of Drug-like Molecules Pharmaceutical Research. ,vol. 25, pp. 1836- 1845 ,(2008) , 10.1007/S11095-008-9584-5
B. Brooks, M. Karplus, Harmonic dynamics of proteins: normal modes and fluctuations in bovine pancreatic trypsin inhibitor. Proceedings of the National Academy of Sciences of the United States of America. ,vol. 80, pp. 6571- 6575 ,(1983) , 10.1073/PNAS.80.21.6571
Igor V. Tetko, Iurii Sushko, Anil Kumar Pandey, Hao Zhu, Alexander Tropsha, Ester Papa, Tomas Öberg, Roberto Todeschini, Denis Fourches, Alexandre Varnek, Critical Assessment of QSAR Models of Environmental Toxicity against Tetrahymena pyriformis: Focusing on Applicability Domain and Overfitting by Variable Selection Journal of Chemical Information and Modeling. ,vol. 48, pp. 1733- 1746 ,(2008) , 10.1021/CI800151M
Robin A.E. Carr, Miles Congreve, Christopher W. Murray, David C. Rees, Fragment-based lead discovery: leads by design Drug Discovery Today. ,vol. 10, pp. 987- 992 ,(2005) , 10.1016/S1359-6446(05)03511-7
Marko Matic, Andre Mahns, Maria Tsoli, Anthony Corradin, Patsie Polly, Graham R. Robertson, Pregnane X receptor: promiscuous regulator of detoxification pathways. The International Journal of Biochemistry & Cell Biology. ,vol. 39, pp. 478- 483 ,(2007) , 10.1016/J.BIOCEL.2006.08.017
Xiaotao Qu, Rosemarie Swanson, Ryan Day, Jerry Tsai, A Guide to Template Based Structure Prediction Current Protein & Peptide Science. ,vol. 10, pp. 270- 285 ,(2009) , 10.2174/138920309788452182
David L Wheeler, Deanna M Church, Ron Edgar, Scott Federhen, Wolfgang Helmberg, Thomas L Madden, Joan U Pontius, Gregory D Schuler, Lynn M Schriml, Edwin Sequeira, Tugba O Suzek, Tatiana A Tatusova, Lukas Wagner, None, Database resources of the National Center for Biotechnology Information: update Nucleic Acids Research. ,vol. 32, pp. 35D- 40 ,(2004) , 10.1093/NAR/GKH073