PMID- 18338230
OWN - NLM
STAT- MEDLINE
DCOM- 20080805
LR  - 20211020
IS  - 0920-654X (Print)
IS  - 0920-654X (Linking)
VI  - 22
IP  - 6-7
DP  - 2008 Jun-Jul
TI  - Categorical QSAR models for skin sensitization based on local lymph node assay 
      measures and both ground and excited state 4D-fingerprint descriptors.
PG  - 345-66
LID - 10.1007/s10822-008-9190-y [doi]
AB  - In previous studies we have developed categorical QSAR models for predicting 
      skin-sensitization potency based on 4D-fingerprint (4D-FP) descriptors and in 
      vivo murine local lymph node assay (LLNA) measures. Only 4D-FP derived from the 
      ground state (GMAX) structures of the molecules were used to build the QSAR 
      models. In this study we have generated 4D-FP descriptors from the first excited 
      state (EMAX) structures of the molecules. The GMAX, EMAX and the combined ground 
      and excited state 4D-FP descriptors (GEMAX) were employed in building categorical 
      QSAR models. Logistic regression (LR) and partial least square coupled logistic 
      regression (PLS-CLR), found to be effective model building for the LLNA 
      skin-sensitization measures in our previous studies, were used again in this 
      study. This also permitted comparison of the prior ground state models to those 
      involving first excited state 4D-FP descriptors. Three types of categorical QSAR 
      models were constructed for each of the GMAX, EMAX and GEMAX datasets: a binary 
      model (2-state), an ordinal model (3-state) and a binary-binary model 
      (two-2-state). No significant differences exist among the LR 2-state model 
      constructed for each of the three datasets. However, the PLS-CLR 3-state and 
      2-state models based on the EMAX and GEMAX datasets have higher predictivity than 
      those constructed using only the GMAX dataset. These EMAX and GMAX categorical 
      models are also more significant and predictive than corresponding models built 
      in our previous QSAR studies of LLNA skin-sensitization measures.
FAU - Liu, Jianzhong
AU  - Liu J
AD  - College of Pharmacy, 1 University of New Mexico, MSC09 5360, Albuquerque, NM, 
      87131-0001, USA.
FAU - Kern, Petra S
AU  - Kern PS
FAU - Gerberick, G Frank
AU  - Gerberick GF
FAU - Santos-Filho, Osvaldo A
AU  - Santos-Filho OA
FAU - Esposito, Emilio X
AU  - Esposito EX
FAU - Hopfinger, Anton J
AU  - Hopfinger AJ
FAU - Tseng, Yufeng J
AU  - Tseng YJ
LA  - eng
GR  - 1 R21 GM075775-01/GM/NIGMS NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, Non-U.S. Gov't
DEP - 20080313
PL  - Netherlands
TA  - J Comput Aided Mol Des
JT  - Journal of computer-aided molecular design
JID - 8710425
SB  - IM
MH  - Humans
MH  - Logistic Models
MH  - Lymph Nodes/*drug effects
MH  - *Models, Molecular
MH  - Quantitative Structure-Activity Relationship
MH  - Skin/*drug effects
EDAT- 2008/03/14 09:00
MHDA- 2008/08/06 09:00
CRDT- 2008/03/14 09:00
PHST- 2007/09/28 00:00 [received]
PHST- 2008/01/30 00:00 [accepted]
PHST- 2008/03/14 09:00 [pubmed]
PHST- 2008/08/06 09:00 [medline]
PHST- 2008/03/14 09:00 [entrez]
AID - 10.1007/s10822-008-9190-y [doi]
PST - ppublish
SO  - J Comput Aided Mol Des. 2008 Jun-Jul;22(6-7):345-66. doi: 
      10.1007/s10822-008-9190-y. Epub 2008 Mar 13.