1. The, H.P., G. Casanola-Martin, K. Dieguez-Santana, N. Nguyen-Hai and N.T. Ngoc, 2017. Quantitative structure–activity relationship analysis and virtual screening studies for identifying HDAC2 inhibitors from known HDAC bioactive chemical libraries. SAR QSAR Environ. Res., 28: 199-220.
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2. Marrero-Ponce, Y., Y.G. CastaÜeda, R. Vivas-Reyes, F.M. Vergara and V.J. ArÌn, 2017. Dry selection and wet evaluation for the rational discovery of new anthelmintics. Mol. Phy., 115: 2300-2313.
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3. Huong, T.T.L., L.V. Cuong, P.T. Huong, T.P. Thao and L.T.T. Huong, 2017. Exploration of some indole- based hydroxamic acids as histone deacetylase inhibitors and antitumor agents. Chem. Pap- Slovak Acad. Sci., 71: 1759-1769.
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4. Huong, T.T.L., D.T.M. Dung, N.V. Huan, L.V. Cuong and P.T. Hai, 2017. Novel N-hydroxybenzamides incorporating 2-oxoindoline with unexpected potent histone deacetylase inhibitory effects and antitumor cytotoxicity. Bioorg. Chem., 71: 160-169.
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5. Dieguez-Santana, K., H. Pham-The, O.M. Rivera-Borroto, A. Puris, H. Le-Thi-Thu and G.M. CasaÜola-Martin, 2017. A two QSAR way for antidiabetic agents targeting using α-amylase and α-glucosidase Inhibitors: Model parameters settings in artificial intelligence techniques. Lett. Drug Des. Discovery, 14: 862-868.
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6. Castillo-Garit, J.A., G.M. CasaÜola-Martin, H. Le-Thi-Thu, H. Pham-The and S. Jones-Barigye, 2017. A simple method to predict blood-brain barrier permeability of drug- like compounds using Med. Chem., 13: 664-669.
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7. Santana, K.D., H. Pham-The, P.J. Villegas, H.L.T. Thu, J.A.C. Garit and G.M. CasaÜola Martin, 2016. Prediction of acute toxicity of phenol derivatives using multiple linear regression approach for Tetrahymena pyriformis contaminant identification in a median-size database. Chemosphere, 165: 434-441.
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8. MartØnez-Santiago, O., Y. Marrero-Ponce, S.J. Barigye, H.L.T. Thu and F.J. Torres et al., 2016. Physico-chemical and structural interpretation of discrete derivative indices on n-tuples atoms. Int. J. Mol. Sci., 17: 812-825.
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9. MartØnez-Santiago, O., R.M. Cabrera, Y. Marrero-Ponce, S. J. Barigye and H. Le-Thi-Thu et al., 2016. Generalized molecular descriptors derived from event-based discrete derivative. Curr. Pharm. Design, 22: 5095-5113.
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10. Tung, B.T., E. Rodriguez-Bies, H.N. Thanh, H. Le-Thi-Thu, P. Navas, V.M. Sanchez and G. Lopez-Lluch, 2015. Organ and tissue-dependent effect of resveratrol and exercise on antioxidant defenses of old mice. Aging Clin. Exp. Res., 27: 775-783.
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11. Thu, H.L.T., Y. Canizares-Carmenate, Y. Marrero-Ponce, F. Torrens and J.A. Castillo-Garit, 2015. Prediction of Caco-2 cell permeability using bilinear indices and multiple linear regression. Lett. Drug Design Discov., 12: 161-169.
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12. Thanh, T.B., H.N. Thanh, H.P.T. Minh, H. Le-Thi-Thu, H.D.T. Ly and L.V. Duc, 2015. Protective effect of Tetracera scandens L. leaf extract against CCl4-induced acute liver injury in rats. Asian Pac. J. Trop. Biomed., 5: 221-227.
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13. Thanh, T.B., H.N. Thanh, H.D.T. Ly, H. Le-Thi-Thu, L.V. Duc and T.N. Huu, 2015. Flavonoids from leaves of Tetracera scandens L. J. Chem. Pharma. Res., 7: 2123-2126.
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14. Pham-The, H., G. CasaÜola-Martin, T. Garrigues, M. Bermejo and I. GonzÌlez-ìlvarez, et al., 2015. Exploring different strategies for imbalanced ADME data problem: Case study on Caco-2 permeability modeling. Mol. Diversity, 20: 93-109.
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15. Marrero-Ponce, Y., C.R. Garcia-Jacas, S.J. Barigye, J.R. Valdes-Martini and O.M. Rivera-Borroto et al., 2015. Optimum search strategies or novel 3D molecular descriptors: Is there a Stalemate?. Curr. Bioinf., 10: 533-564.
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16. H.L.T. Thu, I.B. Cruz, Y. Marrero-Ponce, N. Nguyen-Hai and H. Pham-The et al., 2015. The best choice for the modeling of chemicals against hyper-pigmentation?. Curr. Bioinf., 10: 520-532.
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17. G.M. CasaÜola-Martin, H. Le-Thi-Thu, F. Perez-Gimenez, Y. Marrero-Ponce, M. Merino-SanjuÌn, C. Abad and H. GonzÌlez-DØaz, 2015. Multi-output model with Box–Jenkins operators of linear indices to predict multi-target inhibitors of ubiquitin–proteasome pathway. Mol. Diversity, 19: 347-356.
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18. Dung, D.T.M., P.T.P. Dung, D.T.K. Oanh, H. Pham-The and H. Le-Thi-Thu et al., 2015. Novel 3- substituted-2-oxoindoline-based N-hydroxypropenamides as histone deacetylase inhibitors and ntitumor Agents. Med. Chem., 11: 725-735.
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19. Dung D.T., P.T. Dung, D.T. Oanh, P.T. Hai and L.T. Huong et al., 2015. Novel 3-substituted-2-oxoindoline-based N-hydroxypropenamides as histone deacetylase inhibitors and antitumor agents. Med. Chem., 11: 725-735.
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20. CasaÜola-Martin, G.M., H. Le-Thi-Thu, F. Perez-Gimenez, Y.M. Ponce, M.M. SanjuÌn, C. Abad and H.G. DØaz, 2015. Multi-output model with box-jenkins operators of quadratic indices for prediction of malaria and cancer inhibitors targeting ubiquitin- proteasome pathway (UPP) proteins. Curr. Protein Pept Sci., 17: 220-227.
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21. Brito-SÌnchez, Y., Y. Marrero-Ponce, S.J. Barigye, C.M. Perez, H. Le-Thi-Thu and A. Cherkasov, 2015. Towards better bbb passage prediction using an extensive and curated data set. Mol. Inf., 34: 308-330.
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22. H. Le-Thi-Thu, G.M Casanola-MartØn, Y. Marrero-Ponce, A. Rescigno, C. Abad and M.T.H. Khan, 2014. A rational workflow for sequential virtual screening of chemical libraries on searching for new tyrosinase inhibitors. Curr. Topics Med. Chem., 14: 1473-1485.
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23. Casanola-Martin, G.M., H. Le-Thi-Thu, Y. Marrero-Ponce, J.A. Castillo-Garit, F. Torrens, F. Perez-Gimenez and C. Abad, 2014. Analysis of proteasome inhibition prediction using atom-based quadratic indices enhanced by machine learning classification techniques. Lett Drug Design Discovery, 11: 705-711.
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24. Casanola-Martin, G.M., H. Le-Thi-Thu, Y. Marrero-Ponce, J.A. Castillo-Garit and F. Torrens et al., 2014. Tyrosinase enzyme: 1. An overview on a pharmacological target. Curr. Topics Med. Chem., 14: 1494-1501.
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25. Pham-The, H., I. Gonzalez-Alvarez, M. Bermejo, T. Garrigues, H. Le-Thi-Thu and M.A. Cabrera-Perez, 2013. The use of rule-based and QSPR approaches in ADME profiling: A case study on Caco-2 permeability. Mol. Inform., 32: 459-479.
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26. Brito-Sanchez, Y., J.A. Castillo-Garit, H. Le-Thi-Thu, Y. Gonzalez-Madariaga, F. Torrens, Y. Marrero-Ponce and J.E. Rodriguez-Borges, 2013. Comparative study to predict toxic modes of action of phenols from molecular structures. SAR QSAR Environ. Res., 24: 235-251.
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27. Tugores, Y.M., A.M. Marcel, Y.M. Ponce, V.J. Aran and J.A.E. GarcØa­Trevijano et al., 2012. Descubrimiento de nuevos antimalÌricos a partir de fÌrmacos conocidos mediante cribado in silico e in vitro. An. de la Real Academia Nacional de Farmacia, 78: 401-416.
28. Tugores, M.Y., A.M. Marcel, M.Y. Ponce, V.J. Aran and J.A.E. Garcia-Trevijano et al., 2012. Discovery of new antimalarials from commercial drugs by in silico and in vitro screening. Real Acad. Nac. Farm., 78: 401-416.
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29. CasaÜola-MartØn, G.M., M.T.H. Khan, H. Le-Thi-Thu, Y. Marrero-Ponce, R. GarcØa-Domenech, F. Torrens and A. Rescigno, 2012. Retrained classification of tyrosinase inhibitors and “In Silico” potency estimation by using atom-type linear Indices: A powerful tool for speed up the discovery of leads. Int. J. Chemoinf. Chem. Eng., Vol. 2. 10.4018/ijcce.2012070104.
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30. Le-Thi-Thu, H., Y. Marrero-Ponce, G.M. Casaòola-Martin, G.C. Cardoso and M.d.C.Chavez et al., 2011. A comparative study of nonlinear machine learning for the “In silico” depiction of tyrosinase inhibitory activity from molecular structure. Mol. Inf., 30: 527-537.
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31. Le-Thi-Thu, H., G.M CasaÜola-MartØn, Y. Marrero-Ponce, A. Rescigno and L. Saso et al., 2011. Novel coumarin-based tyrosinase inhibitors discovered by OECD principles-validated QSAR approach from an enlarged, balanced database. Mol. Diversity, 15: 507-520.
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32. Le-Thi-Thu, H., G.C. Cardoso, G.M. Casanola-Martin, Y. Marrero-Ponce and A. Puris et al., 2010. QSAR models for tyrosinase inhibitory activity description applying modern statistical classification techniques: A comparative study. Chemom. Intell. Lab. Syst., 104: 249-259.
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