Dataset Information

Comprehensive approach for predicting toxicological effects of ionic liquids on several biological systems using unified descriptors.

ABSTRACT: The challenge and opportunity for design of environmentally-benign ionic liquids (ILs) would start from prediction of their toxicological effects on several endpoints solely based on the structural formulas. Especially, a comprehensive yet simple equation able to predict several biological responses to IL toxicity is of much advantage. Therefore, based on 50 toxicity testing systems on ILs a comprehensively approachable prediction method was developed. For the modelling, approximately 1600 toxicity values measured by several biological systems and an amended linear free energy relationship (LFER) model were used. Since the toxicological activities of an IL could be differently described according to sensitivity of toxicity testing systems, the sensitivity of each of toxicity testing systems was also estimated in the modelling. By statistical analysis with the calculated descriptors, a LFER model was built. Also the sensitivity value of each system on the basis of the comprehensively approachable model was numerically estimated. In results, it was observed that the combination of single model and sensitivity terms was able to predict each of 50 toxicological effects of ILs with R(2) of 0.593~0.978, and SE of 0.098~0.699 log unit, and the total data set with R(2) of 0.901 and SE of 0.426 log unit.

SUBMITTER: Cho CW

PROVIDER: S-EPMC5022054 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

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Comprehensive approach for predicting toxicological effects of ionic liquids on several biological systems using unified descriptors.

Cho Chul-Woong CW Stolte Stefan S Yun Yeoung-Sang YS

Scientific reports 20160914

The challenge and opportunity for design of environmentally-benign ionic liquids (ILs) would start from prediction of their toxicological effects on several endpoints solely based on the structural formulas. Especially, a comprehensive yet simple equation able to predict several biological responses to IL toxicity is of much advantage. Therefore, based on 50 toxicity testing systems on ILs a comprehensively approachable prediction method was developed. For the modelling, approximately 1600 toxic ...[more]

PMID: 27624396

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Project description:Poor oral bioavailability is an important parameter accounting for the failure of the drug candidates. Approximately, 50% of developing drugs fail because of unfavorable oral bioavailability. In silico prediction of oral bioavailability (%F) based on physiochemical properties are highly needed. Although many computational models have been developed to predict oral bioavailability, their accuracy remains low with a significant number of false positives. In this study, we present an oral bioavailability model based on systems biological approach, using a machine learning algorithm coupled with an optimal discriminative set of physiochemical properties.The models were developed based on computationally derived 247 physicochemical descriptors from 2279 molecules, among which 969, 605 and 705 molecules were corresponds to oral bioavailability, intestinal absorption (HIA) and caco-2 permeability data set, respectively. The partial least squares discriminate analysis showed 49 descriptors of HIA and 50 descriptors of caco-2 are the major contributing descriptors in classifying into groups. Of these descriptors, 47 descriptors were commonly associated to HIA and caco-2, which suggests to play a vital role in classifying oral bioavailability. To determine the best machine learning algorithm, 21 classifiers were compared using a bioavailability data set of 969 molecules with 47 descriptors. Each molecule in the data set was represented by a set of 47 physiochemical properties with the functional relevance labeled as (+bioavailability/-bioavailability) to indicate good-bioavailability/poor-bioavailability molecules. The best-performing algorithm was the logistic algorithm. The correlation based feature selection (CFS) algorithm was implemented, which confirms that these 47 descriptors are the fundamental descriptors for oral bioavailability prediction.The logistic algorithm with 47 selected descriptors correctly predicted the oral bioavailability, with a predictive accuracy of more than 71%. Overall, the method captures the fundamental molecular descriptors, that can be used as an entity to facilitate prediction of oral bioavailability.

Dataset Information

Comprehensive approach for predicting toxicological effects of ionic liquids on several biological systems using unified descriptors.

Publications

Comprehensive approach for predicting toxicological effects of ionic liquids on several biological systems using unified descriptors.

Similar Datasets

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