PMID- 21598906
OWN - NLM
STAT- MEDLINE
DCOM- 20111021
LR  - 20141120
IS  - 1549-960X (Electronic)
IS  - 1549-9596 (Linking)
VI  - 51
IP  - 6
DP  - 2011 Jun 27
TI  - A comparison of different QSAR approaches to modeling CYP450 1A2 inhibition.
PG  - 1271-80
LID - 10.1021/ci200091h [doi]
AB  - Prediction of CYP450 inhibition activity of small molecules poses an important 
      task due to high risk of drug-drug interactions. CYP1A2 is an important member of 
      CYP450 superfamily and accounts for 15% of total CYP450 presence in human liver. 
      This article compares 80 in-silico QSAR models that were created by following the 
      same procedure with different combinations of descriptors and machine learning 
      methods. The training and test sets consist of 3745 and 3741 inhibitors and 
      noninhibitors from PubChem BioAssay database. A heterogeneous external test set 
      of 160 inhibitors was collected from literature. The studied descriptor sets 
      involve E-state, Dragon and ISIDA SMF descriptors. Machine learning methods 
      involve Associative Neural Networks (ASNN), K Nearest Neighbors (kNN), Random 
      Tree (RT), C4.5 Tree (J48), and Support Vector Machines (SVM). The influence of 
      descriptor selection on model accuracy was studied. The benefits of "bagging" 
      modeling approach were shown. Applicability domain approach was successfully 
      applied in this study and ways of increasing model accuracy through use of 
      applicability domain measures were demonstrated as well as fragment-based model 
      interpretation was performed. The most accurate models in this study achieved 
      values of 83% and 68% correctly classified instances on the internal and external 
      test sets, respectively. The applicability domain approach allowed increasing the 
      prediction accuracy to 90% for 78% of the internal and 17% of the external test 
      sets, respectively. The most accurate models are available online at 
      http://ochem.eu/models/Q5747 .
FAU - Novotarskyi, Sergii
AU  - Novotarskyi S
AD  - eADMET GmbH, Ingolstadter Landstrasse 1, D-85764 Neuherberg, Germany.
FAU - Sushko, Iurii
AU  - Sushko I
FAU - Korner, Robert
AU  - Korner R
FAU - Pandey, Anil Kumar
AU  - Pandey AK
FAU - Tetko, Igor V
AU  - Tetko IV
LA  - eng
PT  - Comparative Study
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20110520
PL  - United States
TA  - J Chem Inf Model
JT  - Journal of chemical information and modeling
JID - 101230060
RN  - 0 (Cytochrome P-450 CYP1A2 Inhibitors)
RN  - 0 (Enzyme Inhibitors)
SB  - IM
MH  - *Artificial Intelligence
MH  - *Cytochrome P-450 CYP1A2 Inhibitors
MH  - Enzyme Inhibitors/chemistry/*pharmacology
MH  - Humans
MH  - Molecular Conformation
MH  - *Quantitative Structure-Activity Relationship
EDAT- 2011/05/24 06:00
MHDA- 2011/10/22 06:00
CRDT- 2011/05/24 06:00
PHST- 2011/05/24 06:00 [entrez]
PHST- 2011/05/24 06:00 [pubmed]
PHST- 2011/10/22 06:00 [medline]
AID - 10.1021/ci200091h [doi]
PST - ppublish
SO  - J Chem Inf Model. 2011 Jun 27;51(6):1271-80. doi: 10.1021/ci200091h. Epub 2011 
      May 20.