CORAL: Binary classifications (active/inactive) for drug-induced liver injury

Toropova, Alla P.; Toropov, Andrey A.

doi:10.1016/j.toxlet.2017.01.011

Cited by 37 publications

(22 citation statements)

References 27 publications

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“…presence of rings is not involved in building model). HARD is a descriptor characterizing SMILES as a whole (24). The C5 and C6 are descriptors characterizing rings in the molecular structure.…”

Section: Methodsmentioning

confidence: 99%

“…The C5 and C6 are descriptors characterizing rings in the molecular structure. These descriptors are calculated with the molecular graph (23)(24)(25). C5 and C6 are codes sensitive to the number of corresponding rings in the molecular structure, the presence (absence) heteroatoms, and the presence (absence) of aromaticity.…”

Section: Methodsmentioning

confidence: 99%

“…The aim of the present study was to improve the CORAL model described in (1) by means of using two approaches, namely (i) using of the Index of Ideality of Correlation (IIC), which is a new criterion for the predictive potential of QSAR models (19)(20)(21)(22); and (ii) using correlation weights, which are related to the presence of different rings in the molecular structure (23)(24)(25).…”

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confidence: 99%

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Predicting Cytotoxicity of 2-Phenylindole Derivatives Against Breast Cancer Cells Using Index of Ideality of Correlation

Toropov

Toropova

2018

Anticancer Res

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Background: Breast cancer is one of the leading types of cancer in women worldwide. Quantitative structureactivity relationship (QSAR) methods play an important role in the search for new anticancer agents. A QSAR model for cytotoxicity against the breast cancer cell line MCF7, based on hybrid optimal descriptors, has been suggested. A modified version of the hybrid descriptor is suggested. Materials and Methods: A QSAR model for the anticancer activity of 2-phenylindole derivatives was built using the Index of Ideality of Correlation (IIC), which is a new criterion for predictive potential. The calculation can be carried out with a modified version of the CORAL software. Results: The model for the anticancer activity suggested here is better than the one described in the literature. Conclusion: Taking into account the data on molecular rings together with the use of new criterion of predictive potential (IIC), the QSAR improves the prediction for anticancer activity. Anticancer drug discovery is a complex and important field of natural sciences. Quantitative structure-activity relationships (QSARs) are not able to provide complete data on molecular architecture required for new anticancer agents, but QSARs can help reduce the time needed and cost of the search for such agents (1). The design of new chemical compounds that are active against the breast cancer cell line MCF7 has several conceptually different approaches. These are the well-known ADMET approach (absorption, distribution, metabolism, excretion, and toxicity) (2); general virtual screening based on 6189 This article is freely accessible online.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

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Predicting Cytotoxicity of 2-Phenylindole Derivatives Against Breast Cancer Cells Using Index of Ideality of Correlation

Toropov

Toropova

2018

Anticancer Res

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“…Moreover, using a computational approach coheres with the current tendency for replacing non-clinical tests with in vitro or in silico alternatives, mandated by the implementation of the "3R" principle [10]. This approach is actively encouraged by public authorities such as the European Chemicals Agency (ECHA) or international organizations such as the Organisation for Economic Co-operation and Development (OECD) [11]. Furthermore, computational models allow rapid prediction of the activity of a large number of substances in virtual screening exercises.…”

Section: Introductionmentioning

confidence: 99%

Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset

Ancuceanu

Hovaneț

Anghel

et al. 2020

IJMS

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Drug-induced liver injury (DILI) remains one of the challenges in the safety profile of both authorized and candidate drugs, and predicting hepatotoxicity from the chemical structure of a substance remains a task worth pursuing. Such an approach is coherent with the current tendency for replacing non-clinical tests with in vitro or in silico alternatives. In 2016, a group of researchers from the FDA published an improved annotated list of drugs with respect to their DILI risk, constituting “the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans” (DILIrank). This paper is one of the few attempting to predict liver toxicity using the DILIrank dataset. Molecular descriptors were computed with the Dragon 7.0 software, and a variety of feature selection and machine learning algorithms were implemented in the R computing environment. Nested (double) cross-validation was used to externally validate the models selected. A total of 78 models with reasonable performance were selected and stacked through several approaches, including the building of multiple meta-models. The performance of the stacked models was slightly superior to other models published. The models were applied in a virtual screening exercise on over 100,000 compounds from the ZINC database and about 20% of them were predicted to be non-hepatotoxic.

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“…Recent published papers reveal that the simplified molecular input line entry system (SMILES) is a substitute to classical QSAR methods and it can be used for the prediction of molecular structures with appropriate end point or activity [34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49]. In all the QSAR models, depending on Monte Carlo optimization method, the pertinent activity is treated as random event [50][51][52][53]. In the light of these facts and in continuation of our work [48,[54][55][56][57][58][59][60][61][62][63] on biological important heterocyclic compounds, we herein report Monte Carlo method based QSAR studies of 73 compounds i. e. thienopyrimidine-based monophosphonate (ThP-MP) and N-containing bisphosphonates N-BPs active against hFPPS using SMILES and graph optimal descriptors.…”

Section: Introductionmentioning

confidence: 99%

QSAR Models for Nitrogen Containing Monophosphonate and Bisphosphonate Derivatives as Human Farnesyl Pyrophosphate Synthase Inhibitors Based on Monte Carlo Method

Kumar

Sindhu³

et al. 2018

Drug Res (Stuttg)

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Human farnesyl pyrophosphate synthase (hFPPS) is a well-settled therapeutic target and it is an enzyme of the mevalonate pathway which catalyzes the biosynthesis of the C-15 isoprenoid farnesyl pyrophosphate. QSAR studies by using Monte Carlo method for human farnesyl pyrophosphate synthase inhibitors has been carried out using balance of correlation technique with Index of ideality correlation. For construction of QSAR models, six random splits were prepared from the data of 73 phosphonates and hybrid optimal descriptors procured from graph (HFG) and SMILES based notations were employed. The developed QSAR models have robustness, good fitting ability, generalizability and internal predictive ability. The external predictive ability has been certified by testing various precedents. The values of R, IIC, Q and ∆R for the best model are 0.9304, 0.9614, 0.9061 and 0.0861 respectively. The developed QSAR models met with the specified standards given in OECD guideline and applicability domain. The structural feature promoters for the end point increase and promoters for end point decrease have been extracted. The predicted pIC for the new proposed compounds have also been reported.

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CORAL: Binary classifications (active/inactive) for drug-induced liver injury

Cited by 37 publications

References 27 publications

Predicting Cytotoxicity of 2-Phenylindole Derivatives Against Breast Cancer Cells Using Index of Ideality of Correlation

Predicting Cytotoxicity of 2-Phenylindole Derivatives Against Breast Cancer Cells Using Index of Ideality of Correlation

Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset

QSAR Models for Nitrogen Containing Monophosphonate and Bisphosphonate Derivatives as Human Farnesyl Pyrophosphate Synthase Inhibitors Based on Monte Carlo Method

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