Sigma-2 receptor ligands QSAR model dataset

Rescifina, Antonio; Floresta, Giuseppe; Marrazzo, Agostino; Parenti, Carmela; Prezzavento, Orazio; Nastasi, Giovanni; Dichiara, Maria; Amata, Emanuele

doi:10.1016/j.dib.2017.06.022

Cited by 36 publications

(17 citation statements)

References 5 publications

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“…Tables , 5, and 6 confirm the ability of the IIC to detect better model in the case of models for drug load capacity values of samples “micelle‐polymer” represented by quasi‐SMILES. Factually, these results confirm important principle: “QSAR is a random event”, i. e. the study of a group of different splits into the training and validation sets is necessary to estimate an approach correctly . Thus, Tables contain statistical quality of corresponding models together with the rating function calculated with Eq.…”

Section: Resultssupporting

confidence: 68%

Does the Index of Ideality of Correlation Detect the Better Model Correctly?

Toropova

Toropov

2019

Molecular Informatics

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This paper is dedicated to Prof. Paola Gramatica on the occasion of her retirement. Abstract:The CORAL software is a tool to build up predictive models for various endpoints by means of Quantitative Structure-Property/Activity Relationships (QSPRs/QSARs). A new criterion for assessment of the predictive potential of QSPR/QSAR models, so-called Index of Ideality of Correlation (IIC) is applied to improve the software. The ability of the IIC to detect models with better predictive potential is checked up with groups of random splits of data into the structured training set and extrenal validation set. To this end, two endpoints are examined (i) Toxicity towards Fathead minnow (Pimephales promelas); and (ii) drug load capasity of samples "micelle-polymer". Applications of the IIC for endpoint represented by traditional Simplified Molecular Input-Line Entry System (SMILES) together with so-called quasi-SMILES has shown the suitability of the IIC be a tool to detect better model.

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Section: Resultssupporting

confidence: 68%

Does the Index of Ideality of Correlation Detect the Better Model Correctly?

Toropova

Toropov

2019

Molecular Informatics

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“…In line with our recent interest in the development of QSAR models and related applications [20][21][22][23][24][25][26][27], we recently produced the first 3D-QSAR model for the description of a dataset of selective and potent FABP4 inhibitors [28,29]. The 3D-QSAR model was then combined with a scaffold-hopping analysis, allowing the design of new potent molecules able to interact with the binding site and inhibit the FABP4; finally, three of the ligands suggested by the scaffold-hopping analysis were synthesized and tested in vitro yielding IC50 values between 3.70 and 5.59 M.…”

Section: Introductionmentioning

confidence: 57%

Computational Tools in the Discovery of FABP4 Ligands: A Statistical and Molecular Modeling Approach

Floresta

Gentile

Perrini

et al. 2019

Preprint

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Small molecule inhibitors of adipocyte fatty-acid binding protein 4 (FABP4) have got interest following the recent publication of their pharmacologically beneficial effects. Recently it comes out that FABP4 is an attractive molecular target for the treatment of type 2 diabetes, other metabolic diseases, and some type of cancers. In the past years, hundreds of effective FABP4 inhibitors have been synthesized and discovered but, unfortunately, none of them is in the clinical research phase. The field of computer-aided drug design seems to be promising and useful for the identification of FABP4 inhibitors; hence, different structure- and ligand-based computational approaches were already performed for their identification. In this paper, we searched for new potentially active FABP4 ligands in the Marine Natural Products (MNP) database. 14,492 compounds were retrieved from this database and filtered through a statistical and computational filter. Seven compounds were suggested by our methodology to possess a potential inhibitory activity upon FABP4 in the range of 79–245 nM. ADMET properties prediction were performed to validate the hypothesis of the interaction with the intended target and to assess the drug-likeness of these derivatives; from these analyses, three molecules resulted as excellent candidates for becoming new drugs.

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“…2D-QSAR models have been developed with the use of the software CORAL [13] , [14] , [15] . Once the splits and sets were determined, nine models were developed and statistical quality recorded.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Comprehensive data on a 2D-QSAR model for Heme Oxygenase isoform 1 inhibitors

et al. 2017

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The data have been obtained from the Heme Oxygenase Database (HemeOxDB) and refined according to the 2D-QSAR requirements. These data provide information about a set of more than 380 Heme Oxygenase-1 (HO-1) inhibitors. The development of the 2D-QSAR model has been undertaken with the use of CORAL software using SMILES, molecular graphs and hybrid descriptors (SMILES and graph together). The 2D-QSAR model regressions for HO-1 half maximal inhibitory concentration (IC50) expressed as pIC50 (pIC50=−LogIC50) are here included. The 2D-QSAR model was also employed to predict the HO-1 pIC50values of the FDA approved drugs that are herewith reported.

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Sigma-2 receptor ligands QSAR model dataset

Cited by 36 publications

References 5 publications

Does the Index of Ideality of Correlation Detect the Better Model Correctly?

Does the Index of Ideality of Correlation Detect the Better Model Correctly?

Computational Tools in the Discovery of FABP4 Ligands: A Statistical and Molecular Modeling Approach

Comprehensive data on a 2D-QSAR model for Heme Oxygenase isoform 1 inhibitors

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