In Silico Prediction of Chemicals Binding to Aromatase with Machine Learning Methods

Du, Hanwen; Cai, Yingchun; Yang, Hong-Chang; Zhang, Hongxiao; Xue, Yuhan; Liu, Guixia; Tang, Yun; Li, Weihua

doi:10.1021/acs.chemrestox.7b00037

Cited by 35 publications

(26 citation statements)

References 58 publications

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“…In our model, MLP contained two hidden layers, each consisting of 128 neurons, with relu as the activation function and learning rate being 0.001. In addition, we searched the batch size as 50, 100, 200, and 300 as the potential parameter by using grid search written in Python scripts. Consensus classifier model: As mentioned in previous studies, the consensus classifier methods combining multiple well‐performed single classifier models had a better prediction accuracy than each single classifier model . Thus, the construction of this classifier used in our study consisted of a three‐layer perceptron neural network of which the classified results are from single models.…”

Section: Methodsmentioning

confidence: 99%

“…Consensus classifier model: As mentioned in previous studies, the consensus classifier methods combining multiple well‐performed single classifier models had a better prediction accuracy than each single classifier model . Thus, the construction of this classifier used in our study consisted of a three‐layer perceptron neural network of which the classified results are from single models.…”

Section: Methodsmentioning

confidence: 99%

“…7) Consensus classifier model:A sm entioned in previous studies, the consensus classifier methods combining multiple well-performed single classifier modelsh ad ab etter prediction accuracyt han each single classifier model. [44] Thus,t he construction of this classifier used in our study consisted of ChemMedChem 2018ChemMedChem , 13,2189ChemMedChem -2201 www.chemmedchem.org at hree-layer perceptronneural network of whicht he classified results are from single models. The parameter of the neuraln etwork was searched by using the grid search method in 10-fold cross-validation.…”

Section: ) Logistic Regression (Lr)mentioning

confidence: 99%

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In Silico Prediction of Blood–Brain Barrier Permeability of Compounds by Machine Learning and Resampling Methods

et al. 2018

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The blood-brain barrier (BBB) as a part of absorption protects the central nervous system by separating the brain tissue from the bloodstream. In recent years, BBB permeability has become a critical issue in chemical ADMET prediction, but almost all models were built using imbalanced data sets, which caused a high false-positive rate. Therefore, we tried to solve the problem of biased data sets and built a reliable classification model with 2358 compounds. Machine learning and resampling methods were used simultaneously for the refinement of models with both 2 D molecular descriptors and molecular fingerprints to represent the chemicals. Through a series of evaluation, we realized that resampling methods such as Synthetic Minority Oversampling Technique (SMOTE) and SMOTE+edited nearest neighbor could effectively solve the problem of imbalanced data sets and that MACCS fingerprint combined with support vector machine performed the best. After the final construction of a consensus model, the overall accuracy rate was increased to 0.966 for the final external data set. Also, the accuracy rate of the model for the test set was 0.919, with an excellent balanced capacity of 0.925 (sensitivity) to predict BBB-positive compounds and of 0.899 (specificity) to predict BBB-negative compounds. Compared with other BBB classification models, our models reduced the rate of false positives and were more robust in prediction of BBB-positive as well as BBB-negative compounds, which would be quite helpful in early drug discovery.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: ) Logistic Regression (Lr)mentioning

confidence: 99%

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In Silico Prediction of Blood–Brain Barrier Permeability of Compounds by Machine Learning and Resampling Methods

et al. 2018

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“…Sometimes, to enhance performance of prediction models, combination of these algorithms is applied. We developed a combined method using an artificial neural network (ANN) model to generate the final combination decision probability, which showed that the combined methods would be superior to “single” methods (Cheng et al, 2011b ; Du et al, 2017 ; Sun et al, 2017 ).…”

Section: Model Building With Machine Learning Methodsmentioning

confidence: 99%

In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts

et al. 2018

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During drug development, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future.

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“…Based on the well-defined structural fragments dictionary, MACCS molecular fingerprint is full of structural information [ 33 ]. In previous studies, MACCS and PubChem fingerprints had also been proven to outperform other fingerprints in classifier models [ 33 , 34 ]. By contrast, the Est fingerprint performed worst when the same machine learning methods were used.…”

Section: Resultsmentioning

confidence: 99%

QSAR and Classification Study on Prediction of Acute Oral Toxicity of N-Nitroso Compounds

Fan

Sun

Zhao

et al. 2018

IJMS

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To better understand the mechanism of in vivo toxicity of N-nitroso compounds (NNCs), the toxicity data of 80 NNCs related to their rat acute oral toxicity data (50% lethal dose concentration, LD50) were used to establish quantitative structure-activity relationship (QSAR) and classification models. Quantum chemistry methods calculated descriptors and Dragon descriptors were combined to describe the molecular information of all compounds. Genetic algorithm (GA) and multiple linear regression (MLR) analyses were combined to develop QSAR models. Fingerprints and machine learning methods were used to establish classification models. The quality and predictive performance of all established models were evaluated by internal and external validation techniques. The best GA-MLR-based QSAR model containing eight molecular descriptors was obtained with Q2loo = 0.7533, R2 = 0.8071, Q2ext = 0.7041 and R2ext = 0.7195. The results derived from QSAR studies showed that the acute oral toxicity of NNCs mainly depends on three factors, namely, the polarizability, the ionization potential (IP) and the presence/absence and frequency of C–O bond. For classification studies, the best model was obtained using the MACCS keys fingerprint combined with artificial neural network (ANN) algorithm. The classification models suggested that several representative substructures, including nitrile, hetero N nonbasic, alkylchloride and amine-containing fragments are main contributors for the high toxicity of NNCs. Overall, the developed QSAR and classification models of the rat acute oral toxicity of NNCs showed satisfying predictive abilities. The results provide an insight into the understanding of the toxicity mechanism of NNCs in vivo, which might be used for a preliminary assessment of NNCs toxicity to mammals.

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In Silico Prediction of Chemicals Binding to Aromatase with Machine Learning Methods

Cited by 35 publications

References 58 publications

In Silico Prediction of Blood–Brain Barrier Permeability of Compounds by Machine Learning and Resampling Methods

In Silico Prediction of Blood–Brain Barrier Permeability of Compounds by Machine Learning and Resampling Methods

In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts

QSAR and Classification Study on Prediction of Acute Oral Toxicity of N-Nitroso Compounds

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