In Silico Prediction of Chemical Toxicity Profile Using Local Lazy Learning.
Comb Chem High Throughput Screen
; 20(4): 346-353, 2017.
Article
em En
| MEDLINE
| ID: mdl-28215144
ABSTRACT
BACKGROUND:
Chemical toxicity is an important reason for late-stage failure in drug R&D. However, it is time-consuming and expensive to identify the multiple toxicities of compounds using the traditional experiments. Thus, it is attractive to build an accurate prediction model for the toxicity profile of compounds. MATERIALS ANDMETHODS:
In this study, we carried out a research on six types of toxicities (I) Acute Toxicity; (II) Mutagenicity; (III) Tumorigenicity; (IV) Skin and Eye Irritation; (V) Reproductive Effects; (VI) Multiple Dose Effects, using local lazy learning (LLL) method for multi-label learning. 17,120 compounds were split into the training set and the test set as a ratio of 41 by using the Kennard-Stone algorithm. Four types of properties, including molecular fingerprints (ECFP_4 and FCFP_4), descriptors, and chemical-chemical-interactions, were adopted for model building.RESULTS:
The model 'ECFP_4+LLL' yielded the best performance for the test set, while balanced accuracy (BACC) reached 0.692, 0.691, 0.666, 0.680, 0.631, 0.599 for six types of toxicities, respectively. Furthermore, some essential toxicophores for six types of toxicities were identified by using the Laplacian-modified Bayesian model.CONCLUSION:
The accurate prediction model and the chemical toxicophores can provide some guidance for designing drugs with low toxicity.Palavras-chave
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Base de dados:
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Assunto principal:
Simulação por Computador
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Preparações Farmacêuticas
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Carcinógenos
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Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos
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Descoberta de Drogas
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Modelos Biológicos
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Mutagênicos
Idioma:
En
Ano de publicação:
2017
Tipo de documento:
Article