TOMOCOMD-CARDD descriptors-based virtual screening of tyrosinase inhibitors: Evaluation of different classification model combinations using bond-based linear indices

Casañola-Martín, Gerardo M.; Marrero-Ponce, Yovani; Khan, Mahmud Tareq Hassan; Ather, Arjumand; Sultan, Sadia; Torrens, Francisco; Rotondo, Richard

doi:10.1016/j.bmc.2006.10.067

Cited by 74 publications

(52 citation statements)

References 49 publications

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“…This ALMA-entropy model present excellent performance in both training and external validation series with Sensitivity (Sn), Specificity (Sp), and Accuracy (Ac) > 80%. Values higher than 75% are acceptable for LDA-QSAR models, according to previous reports [9][10][11][12][13].…”

Section: Introductionsupporting

confidence: 71%

<strong>Multi-target Prediction of Neuroprotective Drugs, Synthesis, Assay, and Theoretical Study of Rasagiline Carbamates</strong>

Durán

Alonso

Caamaño

et al. 2015

Proceedings of MOL2NET, International Conference on Multidisciplinary Sciences

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Abstract:In this work, we developed a multi-target model for neuroprotective compounds reported in the CHEMBL database. The model predicted correctly >8300 experimental outcomes with Accuracy, Specificity, and Sensitivity above 80-90% in training and external validation series. This is model can different outcomes for >30 experimental measures in >400 different experimental protocols and related to >150 molecular and cellular targets present in 11 different organisms (including human). After that, we reported by the first time, the synthesis, characterization, and experimental assays of new series of chiral 1,2-rasagiline carbamate derivatives; not reported in previous works. This work is a synopsis of the results presented in our previous paper: Int

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Section: Introductionsupporting

confidence: 71%

<strong>Multi-target Prediction of Neuroprotective Drugs, Synthesis, Assay, and Theoretical Study of Rasagiline Carbamates</strong>

Durán

Alonso

Caamaño

et al. 2015

Proceedings of MOL2NET, International Conference on Multidisciplinary Sciences

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“…Earlier publications outlined the theory of all these 2D TOMOCOMD-CARDD descriptors. 7,[14][15][16][17] …”

Section: Computational Approachmentioning

confidence: 99%

Atom- and Bond-Based 2D TOMOCOMD-CARDD Approach and Ligand-Based Virtual Screening for the Drug Discovery of New Tyrosinase Inhibitors

Casañola-Martín¹,

Marrero-Ponce²,

Khan³

et al. 2008

SLAS Discovery

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Two-dimensional atom-and bond-based TOMOCOMD-CARDD descriptors and linear discriminant analysis (LDA) are used in this report to perform a quantitative structure-activity relationship (QSAR) study of tyrosinase-inhibitory activity. A database of inhibitors of the enzyme is collected for this study, within 246 highly dissimilar molecules presenting antityrosinase activity. In total, 7 discriminant functions are obtained by using the whole set of atom-and bond-based 2D indices. All the LDA-based QSAR models show accuracies above 90% in the training set and values of the Matthews correlation coefficient (C) varying from 0.85 to 0.90. The external validation set shows globally good classifications between 89% and 91% and C values ranging from 0.75 to 0.81. Finally, QSAR models are used in the selection/identification of the 20 new dicoumarins subset to search for tyrosinase inhibitory activity. Theoretical and experimental results show good correspondence between one another. It is important to remark that most compounds in this series exhibit a more potent inhibitory activity against the mushroom tyrosinase enzyme than the reference compound, Kojic acid (IC 50 = 16.67 μM), resulting in a novel nucleus base (lead) with antityrosinase activity, and this could serve as a starting point for the drug discovery of novel tyrosinase inhibitor lead compounds. (Journal of Biomolecular Screening 2008:1014-1024 Key words: TOMOCOMD-CARDD descriptor, LDA-based QSAR model, tyrosinase-inhibitor compound, ligand-based virtual screening, dicoumarinChemoinformatics is the combination of chemical synthesis, biological screening and data analysis to guide drug discovery and development.Blake FJ. Integrating cheminformatic analysis in combinatorial chemistry.

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“…This in silico-method has been successfully applied to the prediction of several physical and chemical properties of organic compounds [12,56,57]. These MDs, and their stochastic forms [12,58], have also been useful in the selection of novel subsystems of compounds with desired properties/activities in virtual screening protocols [59][60][61][62][63][64]. In addition, the molecular linear indices (2D) have been extended to consider three-dimensional features of small/medium-sized molecules based on the trigonometric-3D-chirality-correction factor approach (2.5 GBT-like indices) [53,65].…”

Section: Introductionmentioning

confidence: 99%

Novel global and local 3D atom-based linear descriptors of the Minkowski distance matrix: theory, diversity–variability analysis and QSPR applications

et al. 2015

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A new family of alignment-free 3D descriptors based on TOMOCOMD-CARDD framework has been designed, namely 3D-linear indices. In this report, we have proposed the use of a generalized form of the geometric pairwise atom-atom Electronic supplementary material The online version of this article (123 J Math Chem distance matrix as structural information matrix. This matrix, denominated as nonstochastic, uses as matrix form of linear maps as well as their algebraic transformations: stochastic, double stochastic and mutual probabilities matrices. The methodology for 3D-QSAR studies is based on the combined use of global and local approaches. Principal component analysis reveals that the novel indices are capable of capturing structural information not codified by the indices implemented in the DRAGON's software. Moreover, Shannon's entropy based variability analysis comparing the 3D-linear indices with some relevant descriptors suggests that the former encode similar-to-better amount of structural information than these descriptors. Finally, a search for the best regressions for congeneric databases in QSPR modeling was performed. The overall results demonstrates satisfactory behavior.

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TOMOCOMD-CARDD descriptors-based virtual screening of tyrosinase inhibitors: Evaluation of different classification model combinations using bond-based linear indices

Cited by 74 publications

References 49 publications

<strong>Multi-target Prediction of Neuroprotective Drugs, Synthesis, Assay, and Theoretical Study of Rasagiline Carbamates</strong>

<strong>Multi-target Prediction of Neuroprotective Drugs, Synthesis, Assay, and Theoretical Study of Rasagiline Carbamates</strong>

Atom- and Bond-Based 2D TOMOCOMD-CARDD Approach and Ligand-Based Virtual Screening for the Drug Discovery of New Tyrosinase Inhibitors

Novel global and local 3D atom-based linear descriptors of the Minkowski distance matrix: theory, diversity–variability analysis and QSPR applications

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