TOXICITY MODELLING OF SOME ACTIVE COMPOUNDS AGAINST K562 CANCER CELL LINE USING GENETIC ALGORITHM-MULTIPLE LINEAR REGRESSIONS | Zendy

David Ebuka Arthur | Zendy

Open Access

TOXICITY MODELLING OF SOME ACTIVE COMPOUNDS AGAINST K562 CANCER CELL LINE USING GENETIC ALGORITHM-MULTIPLE LINEAR REGRESSIONS

Author(s) -

David Ebuka Arthur

Publication year - 2016

Publication title -

journal of the turkish chemical society section a chemistry

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.179

H-Index - 6

ISSN - 2149-0120

DOI - 10.18596/jotcsa.287335

Subject(s) - chemistry , robustness (evolution) , toxicity , test set , training set , biological system , quantitative structure–activity relationship , algorithm , computational biology , artificial intelligence , stereochemistry , computer science , biochemistry , organic chemistry , biology , gene

This research entails the modelling of the toxicity of anticancer compounds on K562 cell line, where 112 compounds that make up the data set were divided into training and test set to be used for developing and validating the model respectively. The internal and external validation parameter R 2 for the training and test set given as 0.845 and 0.5316 respectively justifies the robustness and the ability of the model to predict toxicity of the compounds. WPSA-3 and minHBint7 molecular descriptor is responsible for about 50% of the overall effect on the model.

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