Estimation of acute oral toxicity in rat using local lazy learning

Background Acute toxicity means the ability of a substance to cause adverse effects within a short period following dosing or exposure, which is usually the first step in the toxicological investigations of unknown substances. The median lethal dose, $ LD_{50} $, is frequently used as a general indi...
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Autor*in:	Lu, Jing [verfasserIn] Peng, Jianlong Wang, Jinan Shen, Qiancheng Bi, Yi Gong, Likun Zheng, Mingyue Luo, Xiaomin Zhu, Weiliang Jiang, Hualiang Chen, Kaixian

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2014

Schlagwörter:	Acute toxicity Local lazy learning Applicability domain Consensus model

Anmerkung:	© Lu et al.; licensee Chemistry Central Ltd. 2014. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (

Übergeordnetes Werk:	Enthalten in: Journal of cheminformatics - London : BioMed Central, 2009, 6(2014), 1 vom: 16. Mai
Übergeordnetes Werk:	volume:6 ; year:2014 ; number:1 ; day:16 ; month:05

Links:	Volltext

DOI / URN:	10.1186/1758-2946-6-26

Katalog-ID:	SPR031341446

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520			\|a Background Acute toxicity means the ability of a substance to cause adverse effects within a short period following dosing or exposure, which is usually the first step in the toxicological investigations of unknown substances. The median lethal dose, $ LD_{50} $, is frequently used as a general indicator of a substance’s acute toxicity, and there is a high demand on developing non-animal-based prediction of $ LD_{50} $. Unfortunately, it is difficult to accurately predict compound $ LD_{50} $ using a single QSAR model, because the acute toxicity may involve complex mechanisms and multiple biochemical processes. Results In this study, we reported the use of local lazy learning (LLL) methods, which could capture subtle local structure-toxicity relationships around each query compound, to develop $ LD_{50} $ prediction models: (a) local lazy regression (LLR): a linear regression model built using k neighbors; (b) SA: the arithmetical mean of the activities of k nearest neighbors; (c) SR: the weighted mean of the activities of k nearest neighbors; (d) GP: the projection point of the compound on the line defined by its two nearest neighbors. We defined the applicability domain (AD) to decide to what an extent and under what circumstances the prediction is reliable. In the end, we developed a consensus model based on the predicted values of individual LLL models, yielding correlation coefficients $ R^{2} $ of 0.712 on a test set containing 2,896 compounds. Conclusion Encouraged by the promising results, we expect that our consensus LLL model of $ LD_{50} $ would become a useful tool for predicting acute toxicity. All models developed in this study are available via http://www.dddc.ac.cn/admetus.
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700	1		\|a Jiang, Hualiang \|4 aut
700	1		\|a Chen, Kaixian \|4 aut
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allfields	10.1186/1758-2946-6-26 doi (DE-627)SPR031341446 (SPR)1758-2946-6-26-e DE-627 ger DE-627 rakwb eng Lu, Jing verfasserin aut Estimation of acute oral toxicity in rat using local lazy learning 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Lu et al.; licensee Chemistry Central Ltd. 2014. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background Acute toxicity means the ability of a substance to cause adverse effects within a short period following dosing or exposure, which is usually the first step in the toxicological investigations of unknown substances. The median lethal dose, $ LD_{50} $, is frequently used as a general indicator of a substance’s acute toxicity, and there is a high demand on developing non-animal-based prediction of $ LD_{50} $. Unfortunately, it is difficult to accurately predict compound $ LD_{50} $ using a single QSAR model, because the acute toxicity may involve complex mechanisms and multiple biochemical processes. Results In this study, we reported the use of local lazy learning (LLL) methods, which could capture subtle local structure-toxicity relationships around each query compound, to develop $ LD_{50} $ prediction models: (a) local lazy regression (LLR): a linear regression model built using k neighbors; (b) SA: the arithmetical mean of the activities of k nearest neighbors; (c) SR: the weighted mean of the activities of k nearest neighbors; (d) GP: the projection point of the compound on the line defined by its two nearest neighbors. We defined the applicability domain (AD) to decide to what an extent and under what circumstances the prediction is reliable. In the end, we developed a consensus model based on the predicted values of individual LLL models, yielding correlation coefficients $ R^{2} $ of 0.712 on a test set containing 2,896 compounds. Conclusion Encouraged by the promising results, we expect that our consensus LLL model of $ LD_{50} $ would become a useful tool for predicting acute toxicity. All models developed in this study are available via http://www.dddc.ac.cn/admetus. Acute toxicity (dpeaa)DE-He213 Local lazy learning (dpeaa)DE-He213 Applicability domain (dpeaa)DE-He213 Consensus model (dpeaa)DE-He213 Peng, Jianlong aut Wang, Jinan aut Shen, Qiancheng aut Bi, Yi aut Gong, Likun aut Zheng, Mingyue aut Luo, Xiaomin aut Zhu, Weiliang aut Jiang, Hualiang aut Chen, Kaixian aut Enthalten in Journal of cheminformatics London : BioMed Central, 2009 6(2014), 1 vom: 16. Mai (DE-627)594779219 (DE-600)2486539-4 1758-2946 nnns volume:6 year:2014 number:1 day:16 month:05 https://dx.doi.org/10.1186/1758-2946-6-26 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2014 1 16 05
spelling	10.1186/1758-2946-6-26 doi (DE-627)SPR031341446 (SPR)1758-2946-6-26-e DE-627 ger DE-627 rakwb eng Lu, Jing verfasserin aut Estimation of acute oral toxicity in rat using local lazy learning 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Lu et al.; licensee Chemistry Central Ltd. 2014. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background Acute toxicity means the ability of a substance to cause adverse effects within a short period following dosing or exposure, which is usually the first step in the toxicological investigations of unknown substances. The median lethal dose, $ LD_{50} $, is frequently used as a general indicator of a substance’s acute toxicity, and there is a high demand on developing non-animal-based prediction of $ LD_{50} $. Unfortunately, it is difficult to accurately predict compound $ LD_{50} $ using a single QSAR model, because the acute toxicity may involve complex mechanisms and multiple biochemical processes. Results In this study, we reported the use of local lazy learning (LLL) methods, which could capture subtle local structure-toxicity relationships around each query compound, to develop $ LD_{50} $ prediction models: (a) local lazy regression (LLR): a linear regression model built using k neighbors; (b) SA: the arithmetical mean of the activities of k nearest neighbors; (c) SR: the weighted mean of the activities of k nearest neighbors; (d) GP: the projection point of the compound on the line defined by its two nearest neighbors. We defined the applicability domain (AD) to decide to what an extent and under what circumstances the prediction is reliable. In the end, we developed a consensus model based on the predicted values of individual LLL models, yielding correlation coefficients $ R^{2} $ of 0.712 on a test set containing 2,896 compounds. Conclusion Encouraged by the promising results, we expect that our consensus LLL model of $ LD_{50} $ would become a useful tool for predicting acute toxicity. All models developed in this study are available via http://www.dddc.ac.cn/admetus. Acute toxicity (dpeaa)DE-He213 Local lazy learning (dpeaa)DE-He213 Applicability domain (dpeaa)DE-He213 Consensus model (dpeaa)DE-He213 Peng, Jianlong aut Wang, Jinan aut Shen, Qiancheng aut Bi, Yi aut Gong, Likun aut Zheng, Mingyue aut Luo, Xiaomin aut Zhu, Weiliang aut Jiang, Hualiang aut Chen, Kaixian aut Enthalten in Journal of cheminformatics London : BioMed Central, 2009 6(2014), 1 vom: 16. Mai (DE-627)594779219 (DE-600)2486539-4 1758-2946 nnns volume:6 year:2014 number:1 day:16 month:05 https://dx.doi.org/10.1186/1758-2946-6-26 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2014 1 16 05
allfields_unstemmed	10.1186/1758-2946-6-26 doi (DE-627)SPR031341446 (SPR)1758-2946-6-26-e DE-627 ger DE-627 rakwb eng Lu, Jing verfasserin aut Estimation of acute oral toxicity in rat using local lazy learning 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Lu et al.; licensee Chemistry Central Ltd. 2014. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background Acute toxicity means the ability of a substance to cause adverse effects within a short period following dosing or exposure, which is usually the first step in the toxicological investigations of unknown substances. The median lethal dose, $ LD_{50} $, is frequently used as a general indicator of a substance’s acute toxicity, and there is a high demand on developing non-animal-based prediction of $ LD_{50} $. Unfortunately, it is difficult to accurately predict compound $ LD_{50} $ using a single QSAR model, because the acute toxicity may involve complex mechanisms and multiple biochemical processes. Results In this study, we reported the use of local lazy learning (LLL) methods, which could capture subtle local structure-toxicity relationships around each query compound, to develop $ LD_{50} $ prediction models: (a) local lazy regression (LLR): a linear regression model built using k neighbors; (b) SA: the arithmetical mean of the activities of k nearest neighbors; (c) SR: the weighted mean of the activities of k nearest neighbors; (d) GP: the projection point of the compound on the line defined by its two nearest neighbors. We defined the applicability domain (AD) to decide to what an extent and under what circumstances the prediction is reliable. In the end, we developed a consensus model based on the predicted values of individual LLL models, yielding correlation coefficients $ R^{2} $ of 0.712 on a test set containing 2,896 compounds. Conclusion Encouraged by the promising results, we expect that our consensus LLL model of $ LD_{50} $ would become a useful tool for predicting acute toxicity. All models developed in this study are available via http://www.dddc.ac.cn/admetus. Acute toxicity (dpeaa)DE-He213 Local lazy learning (dpeaa)DE-He213 Applicability domain (dpeaa)DE-He213 Consensus model (dpeaa)DE-He213 Peng, Jianlong aut Wang, Jinan aut Shen, Qiancheng aut Bi, Yi aut Gong, Likun aut Zheng, Mingyue aut Luo, Xiaomin aut Zhu, Weiliang aut Jiang, Hualiang aut Chen, Kaixian aut Enthalten in Journal of cheminformatics London : BioMed Central, 2009 6(2014), 1 vom: 16. Mai (DE-627)594779219 (DE-600)2486539-4 1758-2946 nnns volume:6 year:2014 number:1 day:16 month:05 https://dx.doi.org/10.1186/1758-2946-6-26 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2014 1 16 05
allfieldsGer	10.1186/1758-2946-6-26 doi (DE-627)SPR031341446 (SPR)1758-2946-6-26-e DE-627 ger DE-627 rakwb eng Lu, Jing verfasserin aut Estimation of acute oral toxicity in rat using local lazy learning 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Lu et al.; licensee Chemistry Central Ltd. 2014. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background Acute toxicity means the ability of a substance to cause adverse effects within a short period following dosing or exposure, which is usually the first step in the toxicological investigations of unknown substances. The median lethal dose, $ LD_{50} $, is frequently used as a general indicator of a substance’s acute toxicity, and there is a high demand on developing non-animal-based prediction of $ LD_{50} $. Unfortunately, it is difficult to accurately predict compound $ LD_{50} $ using a single QSAR model, because the acute toxicity may involve complex mechanisms and multiple biochemical processes. Results In this study, we reported the use of local lazy learning (LLL) methods, which could capture subtle local structure-toxicity relationships around each query compound, to develop $ LD_{50} $ prediction models: (a) local lazy regression (LLR): a linear regression model built using k neighbors; (b) SA: the arithmetical mean of the activities of k nearest neighbors; (c) SR: the weighted mean of the activities of k nearest neighbors; (d) GP: the projection point of the compound on the line defined by its two nearest neighbors. We defined the applicability domain (AD) to decide to what an extent and under what circumstances the prediction is reliable. In the end, we developed a consensus model based on the predicted values of individual LLL models, yielding correlation coefficients $ R^{2} $ of 0.712 on a test set containing 2,896 compounds. Conclusion Encouraged by the promising results, we expect that our consensus LLL model of $ LD_{50} $ would become a useful tool for predicting acute toxicity. All models developed in this study are available via http://www.dddc.ac.cn/admetus. Acute toxicity (dpeaa)DE-He213 Local lazy learning (dpeaa)DE-He213 Applicability domain (dpeaa)DE-He213 Consensus model (dpeaa)DE-He213 Peng, Jianlong aut Wang, Jinan aut Shen, Qiancheng aut Bi, Yi aut Gong, Likun aut Zheng, Mingyue aut Luo, Xiaomin aut Zhu, Weiliang aut Jiang, Hualiang aut Chen, Kaixian aut Enthalten in Journal of cheminformatics London : BioMed Central, 2009 6(2014), 1 vom: 16. Mai (DE-627)594779219 (DE-600)2486539-4 1758-2946 nnns volume:6 year:2014 number:1 day:16 month:05 https://dx.doi.org/10.1186/1758-2946-6-26 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2014 1 16 05
allfieldsSound	10.1186/1758-2946-6-26 doi (DE-627)SPR031341446 (SPR)1758-2946-6-26-e DE-627 ger DE-627 rakwb eng Lu, Jing verfasserin aut Estimation of acute oral toxicity in rat using local lazy learning 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Lu et al.; licensee Chemistry Central Ltd. 2014. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background Acute toxicity means the ability of a substance to cause adverse effects within a short period following dosing or exposure, which is usually the first step in the toxicological investigations of unknown substances. The median lethal dose, $ LD_{50} $, is frequently used as a general indicator of a substance’s acute toxicity, and there is a high demand on developing non-animal-based prediction of $ LD_{50} $. Unfortunately, it is difficult to accurately predict compound $ LD_{50} $ using a single QSAR model, because the acute toxicity may involve complex mechanisms and multiple biochemical processes. Results In this study, we reported the use of local lazy learning (LLL) methods, which could capture subtle local structure-toxicity relationships around each query compound, to develop $ LD_{50} $ prediction models: (a) local lazy regression (LLR): a linear regression model built using k neighbors; (b) SA: the arithmetical mean of the activities of k nearest neighbors; (c) SR: the weighted mean of the activities of k nearest neighbors; (d) GP: the projection point of the compound on the line defined by its two nearest neighbors. We defined the applicability domain (AD) to decide to what an extent and under what circumstances the prediction is reliable. In the end, we developed a consensus model based on the predicted values of individual LLL models, yielding correlation coefficients $ R^{2} $ of 0.712 on a test set containing 2,896 compounds. Conclusion Encouraged by the promising results, we expect that our consensus LLL model of $ LD_{50} $ would become a useful tool for predicting acute toxicity. All models developed in this study are available via http://www.dddc.ac.cn/admetus. Acute toxicity (dpeaa)DE-He213 Local lazy learning (dpeaa)DE-He213 Applicability domain (dpeaa)DE-He213 Consensus model (dpeaa)DE-He213 Peng, Jianlong aut Wang, Jinan aut Shen, Qiancheng aut Bi, Yi aut Gong, Likun aut Zheng, Mingyue aut Luo, Xiaomin aut Zhu, Weiliang aut Jiang, Hualiang aut Chen, Kaixian aut Enthalten in Journal of cheminformatics London : BioMed Central, 2009 6(2014), 1 vom: 16. Mai (DE-627)594779219 (DE-600)2486539-4 1758-2946 nnns volume:6 year:2014 number:1 day:16 month:05 https://dx.doi.org/10.1186/1758-2946-6-26 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2014 1 16 05
language	English
source	Enthalten in Journal of cheminformatics 6(2014), 1 vom: 16. Mai volume:6 year:2014 number:1 day:16 month:05
sourceStr	Enthalten in Journal of cheminformatics 6(2014), 1 vom: 16. Mai volume:6 year:2014 number:1 day:16 month:05
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institution	findex.gbv.de
topic_facet	Acute toxicity Local lazy learning Applicability domain Consensus model
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spellingShingle	Lu, Jing misc Acute toxicity misc Local lazy learning misc Applicability domain misc Consensus model Estimation of acute oral toxicity in rat using local lazy learning
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topic_title	Estimation of acute oral toxicity in rat using local lazy learning Acute toxicity (dpeaa)DE-He213 Local lazy learning (dpeaa)DE-He213 Applicability domain (dpeaa)DE-He213 Consensus model (dpeaa)DE-He213
topic	misc Acute toxicity misc Local lazy learning misc Applicability domain misc Consensus model
topic_unstemmed	misc Acute toxicity misc Local lazy learning misc Applicability domain misc Consensus model
topic_browse	misc Acute toxicity misc Local lazy learning misc Applicability domain misc Consensus model
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title	Estimation of acute oral toxicity in rat using local lazy learning
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title_full	Estimation of acute oral toxicity in rat using local lazy learning
author_sort	Lu, Jing
journal	Journal of cheminformatics
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author_browse	Lu, Jing Peng, Jianlong Wang, Jinan Shen, Qiancheng Bi, Yi Gong, Likun Zheng, Mingyue Luo, Xiaomin Zhu, Weiliang Jiang, Hualiang Chen, Kaixian
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author-letter	Lu, Jing
doi_str_mv	10.1186/1758-2946-6-26
title_sort	estimation of acute oral toxicity in rat using local lazy learning
title_auth	Estimation of acute oral toxicity in rat using local lazy learning
abstract	Background Acute toxicity means the ability of a substance to cause adverse effects within a short period following dosing or exposure, which is usually the first step in the toxicological investigations of unknown substances. The median lethal dose, $ LD_{50} $, is frequently used as a general indicator of a substance’s acute toxicity, and there is a high demand on developing non-animal-based prediction of $ LD_{50} $. Unfortunately, it is difficult to accurately predict compound $ LD_{50} $ using a single QSAR model, because the acute toxicity may involve complex mechanisms and multiple biochemical processes. Results In this study, we reported the use of local lazy learning (LLL) methods, which could capture subtle local structure-toxicity relationships around each query compound, to develop $ LD_{50} $ prediction models: (a) local lazy regression (LLR): a linear regression model built using k neighbors; (b) SA: the arithmetical mean of the activities of k nearest neighbors; (c) SR: the weighted mean of the activities of k nearest neighbors; (d) GP: the projection point of the compound on the line defined by its two nearest neighbors. We defined the applicability domain (AD) to decide to what an extent and under what circumstances the prediction is reliable. In the end, we developed a consensus model based on the predicted values of individual LLL models, yielding correlation coefficients $ R^{2} $ of 0.712 on a test set containing 2,896 compounds. Conclusion Encouraged by the promising results, we expect that our consensus LLL model of $ LD_{50} $ would become a useful tool for predicting acute toxicity. All models developed in this study are available via http://www.dddc.ac.cn/admetus. © Lu et al.; licensee Chemistry Central Ltd. 2014. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (
abstractGer	Background Acute toxicity means the ability of a substance to cause adverse effects within a short period following dosing or exposure, which is usually the first step in the toxicological investigations of unknown substances. The median lethal dose, $ LD_{50} $, is frequently used as a general indicator of a substance’s acute toxicity, and there is a high demand on developing non-animal-based prediction of $ LD_{50} $. Unfortunately, it is difficult to accurately predict compound $ LD_{50} $ using a single QSAR model, because the acute toxicity may involve complex mechanisms and multiple biochemical processes. Results In this study, we reported the use of local lazy learning (LLL) methods, which could capture subtle local structure-toxicity relationships around each query compound, to develop $ LD_{50} $ prediction models: (a) local lazy regression (LLR): a linear regression model built using k neighbors; (b) SA: the arithmetical mean of the activities of k nearest neighbors; (c) SR: the weighted mean of the activities of k nearest neighbors; (d) GP: the projection point of the compound on the line defined by its two nearest neighbors. We defined the applicability domain (AD) to decide to what an extent and under what circumstances the prediction is reliable. In the end, we developed a consensus model based on the predicted values of individual LLL models, yielding correlation coefficients $ R^{2} $ of 0.712 on a test set containing 2,896 compounds. Conclusion Encouraged by the promising results, we expect that our consensus LLL model of $ LD_{50} $ would become a useful tool for predicting acute toxicity. All models developed in this study are available via http://www.dddc.ac.cn/admetus. © Lu et al.; licensee Chemistry Central Ltd. 2014. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (
abstract_unstemmed	Background Acute toxicity means the ability of a substance to cause adverse effects within a short period following dosing or exposure, which is usually the first step in the toxicological investigations of unknown substances. The median lethal dose, $ LD_{50} $, is frequently used as a general indicator of a substance’s acute toxicity, and there is a high demand on developing non-animal-based prediction of $ LD_{50} $. Unfortunately, it is difficult to accurately predict compound $ LD_{50} $ using a single QSAR model, because the acute toxicity may involve complex mechanisms and multiple biochemical processes. Results In this study, we reported the use of local lazy learning (LLL) methods, which could capture subtle local structure-toxicity relationships around each query compound, to develop $ LD_{50} $ prediction models: (a) local lazy regression (LLR): a linear regression model built using k neighbors; (b) SA: the arithmetical mean of the activities of k nearest neighbors; (c) SR: the weighted mean of the activities of k nearest neighbors; (d) GP: the projection point of the compound on the line defined by its two nearest neighbors. We defined the applicability domain (AD) to decide to what an extent and under what circumstances the prediction is reliable. In the end, we developed a consensus model based on the predicted values of individual LLL models, yielding correlation coefficients $ R^{2} $ of 0.712 on a test set containing 2,896 compounds. Conclusion Encouraged by the promising results, we expect that our consensus LLL model of $ LD_{50} $ would become a useful tool for predicting acute toxicity. All models developed in this study are available via http://www.dddc.ac.cn/admetus. © Lu et al.; licensee Chemistry Central Ltd. 2014. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (
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title_short	Estimation of acute oral toxicity in rat using local lazy learning
url	https://dx.doi.org/10.1186/1758-2946-6-26
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author2	Peng, Jianlong Wang, Jinan Shen, Qiancheng Bi, Yi Gong, Likun Zheng, Mingyue Luo, Xiaomin Zhu, Weiliang Jiang, Hualiang Chen, Kaixian
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