Impact of geometry optimization methods on QSAR modelling: A case study for predicting human serum albumin binding affinity

Önlü, Serli; Saçan, Melek Türker

doi:10.1080/1062936x.2017.1343253

Cited by 9 publications

(8 citation statements)

References 39 publications

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“…The DRAGON [ 55 ] software (version 7.0) was used to obtain the 0-2D (two-dimension) molecular descriptors. As most 3D descriptor groups encoding 3D structures were found to be sensitive to the quantum chemical calculation method [ 56 ] which can influence the accuracy of QSAR model, we therefore excluded the 3D descriptors. The total number of 0-2D descriptors was 3822.…”

Section: Methodsmentioning

confidence: 99%

“…Then, GA was utilized to detect the solution space by maximizing the leave-one-out (LOO) cross-validation correlation coefficient (Q 2 loo ) as the fitness function. To obtain the best variables, the population size, mutation rate and number of generations were set as 200, 20 and 2000, respectively [ 23 , 56 ]. Q 2 loo was chosen as it provides a measurement of model stability and robustness.…”

Section: Methodsmentioning

confidence: 99%

“…The Multi-Criteria Decision Making (MCDM) method included in QSARINS 2.2.2 software was used to summarize the model performances relevant to internal and external validations as scores [ 56 , 57 ]. The scores range from 0 to 1, where 0 and 1 represent the worst and the best validation criteria, respectively.…”

Section: Methodsmentioning

confidence: 99%

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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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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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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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“…After structure optimization, Dragon descriptors were calculated by DRAGON software (version 7.0) [ 50 ]. Due to most of 3D descriptors encoding 3D structures were found to be sensitive to the quantum chemical calculation method which may influence the quality of QSAR model, thus we removed the 3D descriptors [ 51 ]. DRAGON 7.0 contains 22 2D molecular descriptor blocks (e.g., constitutional indices, ring descriptors, topological indices, connectivity indices, and so on), which consist of a total of 3822 0-2D descriptors.…”

Section: Methodsmentioning

confidence: 99%

In Silico Prediction of O6-Methylguanine-DNA Methyltransferase Inhibitory Potency of Base Analogs with QSAR and Machine Learning Methods

et al. 2018

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O6-methylguanine-DNA methyltransferase (MGMT), a unique DNA repair enzyme, can confer resistance to DNA anticancer alkylating agents that modify the O6-position of guanine. Thus, inhibition of MGMT activity in tumors has a great interest for cancer researchers because it can significantly improve the anticancer efficacy of such alkylating agents. In this study, we performed a quantitative structure activity relationship (QSAR) and classification study based on a total of 134 base analogs related to their ED50 values (50% inhibitory concentration) against MGMT. Molecular information of all compounds were described by quantum chemical descriptors and Dragon descriptors. Genetic algorithm (GA) and multiple linear regression (MLR) analysis were combined to develop QSAR models. Classification models were generated by seven machine-learning methods based on six types of molecular fingerprints. Performances of all developed models were assessed by internal and external validation techniques. The best QSAR model was obtained with Q2Loo = 0.83, R2 = 0.87, Q2ext = 0.67, and R2ext = 0.69 based on 84 compounds. The results from QSAR studies indicated topological charge indices, polarizability, ionization potential (IP), and number of primary aromatic amines are main contributors for MGMT inhibition of base analogs. For classification studies, the accuracies of 10-fold cross-validation ranged from 0.750 to 0.885 for top ten models. The range of accuracy for the external test set ranged from 0.800 to 0.880 except for PubChem-Tree model, suggesting a satisfactory predictive ability. Three models (Ext-SVM, Ext-Tree and Graph-RF) showed high and reliable predictive accuracy for both training and external test sets. In addition, several representative substructures for characterizing MGMT inhibitors were identified by information gain and substructure frequency analysis method. Our studies might be useful for further study to design and rapidly identify potential MGMT inhibitors.

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“…Other studies focused on albumin serum affinity (HSA) with methods as SVM or HA ( 8 ) or tried to integrate QSAR and docking scores ( 9 ), including geometry optimization before modeling to improve performance ( 10 ).…”

Section: Introductionmentioning

confidence: 99%

QSAR Development for Plasma Protein Binding: Influence of the Ionization State

et al. 2018

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PurposeThis study explored several strategies to improve the performance of literature QSAR models for plasma protein binding (PPB), such as a suitable endpoint transformation, a correct representation of chemicals, more consistency in the dataset, and a reliable definition of the applicability domain.MethodsWe retrieved human fraction unbound (Fu) data for 670 compounds from the literature and carefully checked them for consistency. Descriptors were calculated taking account of the ionization state of molecules at physiological pH (7.4), in order to better estimate the affinity of molecules to blood proteins. We used different algorithms and chemical descriptors to explore the most suitable strategy for modeling the endpoint. SMILES (simplified molecular input line entry system)-based string descriptors were also tested with the CORAL software (CORelation And Logic). We did an outlier analysis to establish the models to use (or not to use) in case of well recognized families.ResultsInternal validation of the selected models returned Q2 values close to 0.60. External validation also gave r2 values always greater than 0.60. The CORAL descriptor based model for √fu was the best, with r2 0.74 in external validation.ConclusionsPerformance in prediction confirmed the robustness of all the derived models and their suitability for real-life purposes, i.e. screening chemicals for their ADMET profiling. Optimization of descriptors can be useful in order to obtain the correct results with a ionized molecule.Electronic supplementary materialThe online version of this article (10.1007/s11095-018-2561-8) contains supplementary material, which is available to authorized users.

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Impact of geometry optimization methods on QSAR modelling: A case study for predicting human serum albumin binding affinity

Cited by 9 publications

References 39 publications

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

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

In Silico Prediction of O6-Methylguanine-DNA Methyltransferase Inhibitory Potency of Base Analogs with QSAR and Machine Learning Methods

QSAR Development for Plasma Protein Binding: Influence of the Ionization State

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