Electronic structure and photoreactivity of the triplet states of skin-sensitizing psoralens

Song, Pill‐Soon; Chin, Chen-An; Yamazaki, Iwao; Baba, Hiroaki

doi:10.1002/qua.560090702

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…Studies [2,3] show that the photoreactive states of skinsensitizing carcinogenic compounds are characterized by a substantial localization of the electronic excitation, thereby providing a logical basis for the structure-activity correlation of skin-sensitizing compounds. And in an analogous manner, the coumarins, which are one class of typical skinsensitizing derivatives, also possess partially localized triplet states [4].…”

Section: Introductionmentioning

confidence: 99%

“…The two primary goals of this study are: (1) to develop a methodology to compute 4D-FP from the electronic and geometric structures of molecules in their excited states, and, (2) to use ground and/or excited state 4D-FP to build categorical QSAR models for skin sensitization that can be directly compared to equivalent models previously developed using only ground state 4D-FP. To facilitate the second goal-generating 4D-FP categorical QSAR models, three types of 4D-FP descriptors have been assigned to the descriptor pool: GMAX (ground state descriptors only), EMAX (excited state descriptors only), and GEMAX (the combination of ground and excited state descriptors).…”

Section: Introductionmentioning

confidence: 99%

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Categorical QSAR models for skin sensitization based on local lymph node assay measures and both ground and excited state 4D-fingerprint descriptors

Liu

Kern

Gerberick

et al. 2008

J Comput Aided Mol Des

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%