• YAKHAK HOEJI
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• v.46 no.4
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• pp.219-225
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• 2002

We have performed a 3D-QSAR/CoMFA analysis of the cytotoxic activities of thirty-five 3-arylisoquinoline derivatives against SK-OV-3 tumor cell line. The results suggested that the electrostatic, steric and hydrophobic factors of 3-arylisoquinolines were strongly correlated with the antitumor activity. Considerable predictive ability (cross-validated r2 as high as 0.841) was obtained through CoMFA.

• Proceedings of the PSK Conference
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• 2003.10b
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• pp.171.1-171.1
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• 2003

·The potent antitumor activities of 3-arylisoquinolines promoted us to explore the structure-activity relationship of these compounds. A series of 3-arylisoquinoline derivatives were evaluated for antitumor cytotoxicity against human lung tumor cell (A 549). For the next stage, we decided to prepare the constrained form of 3-arylisoquinolines as indeno[1,2-c]isoquinolines. As a result, diverse spectrum against human tumor cell lines was obtained. In order to study structure-activity relationship (SAT) of these compounds the comparative molecular field analysis (CoMFA) was carried out. (omitted)

• Proceedings of the PSK Conference
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• 2002.10a
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• pp.348.2-348.2
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• 2002

The significant antitumor activities of 3-arylisoquinolines promoted us to explore the structure-activity relationship of these compounds. A series of 3-Arylisoquinoline derivatives, which related to Benzo［c］ phenanthridine alkaloids. were evaluated for antitumor cytotoxicity against human lung tumor cell (A 549). We tried to study structure-activity relationship (SAR) of 3-Arylisoquinolines using the comparative molecular field analysis (CoMFA) method. (omitted)

• Proceedings of the PSK Conference
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• 2000.10a
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• pp.217.2-217.2
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• 2000

• Proceedings of the PSK Conference
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• 2001.04a
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• pp.106.1-106.1
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• 2001

• Bulletin of the Korean Chemical Society
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• v.32 no.7
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• pp.2397-2404
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• 2011

Variable selection k nearest neighbor QSAR modeling approach was applied to a data set of 80 3-arylisoquinolines exhibiting cytotoxicity against human lung tumor cell line (A-549). All compounds were characterized with molecular topology descriptors calculated with the MolconnZ program. Seven compounds were randomly selected from the original dataset and used as an external validation set. The remaining subset of 73 compounds was divided into multiple training (56 to 61 compounds) and test (17 to 12 compounds) sets using a chemical diversity sampling method developed in this group. Highly predictive models characterized by the leave-one out cross-validated $R^2$ ($q^2$) values greater than 0.8 for the training sets and $R^2$ values greater than 0.7 for the test sets have been obtained. The robustness of models was confirmed by the Y-randomization test: all models built using training sets with randomly shuffled activities were characterized by low $q^2{\leq}0.26$ and $R^2{\leq}0.22$ for training and test sets, respectively. Twelve best models (with the highest values of both $q^2$ and $R^2$) predicted the activities of the external validation set of seven compounds with $R^2$ ranging from 0.71 to 0.93.