Application 2D Descriptors and Artificial Neural Networks for Beta-Glucosidase Inhibitors Screening - Publication - MOST Wiedzy

Search

Application 2D Descriptors and Artificial Neural Networks for Beta-Glucosidase Inhibitors Screening

Abstract

Beta-glucosidase inhibitors play important medical and biological roles. In this study, simple two-variable artificial neural network (ANN) classification models were developed for beta-glucosidase inhibitors screening. All bioassay data were obtained from the ChEMBL database. The classifiers were generated using 2D molecular descriptors and the data miner tool available in the STATISTICA package (STATISTICA Automated Neural Networks, SANN). In order to evaluate the models’ accuracy and select the best classifiers among automatically generated SANNs, the Matthews correlation coefficient (MCC) was used. The application of the combination of maxHBint3 and SpMax8_Bhs descriptors leads to the highest predicting abilities of SANNs, as evidenced by the averaged test set prediction results (MCC = 0.748) calculated for ten different dataset splits. Additionally, the models were analyzed employing receiver operating characteristics (ROC) and cumulative gain charts. The thirteen final classifiers obtained as a result of the model development procedure were applied for a natural compounds collection available in the BIOFACQUIM database. As a result of this beta-glucosidase inhibitors screening, eight compounds were univocally classified as active by all SANNs.

Citations

  • 0

    CrossRef

  • 0

    Web of Science

  • 0

    Scopus

Author (1)

Details

Category:
Magazine publication
Type:
Magazine publication
Published in:
MOLECULES no. 25, edition 24,
ISSN: 1420-3049
ISSN:
1420-3049
Publication year:
2020
DOI:
Digital Object Identifier (open in new tab) 10.3390/molecules25245942
Bibliography: test
  1. De Melo, B.E.; Da Silveira, G.A.; Carvalho, I. α-and β-Glucosidase inhibitors: Chemical structure and biological activity. Tetrahedron 2006, 62, 10277-10302. [CrossRef] open in new tab
  2. Campo, V.L.; Aragão-Leoneti, V.; Carvalho, I. Glycosidases and diabetes: Metabolic changes, mode of action and therapeutic perspectives. In Carbohydrate Chemistry; Royal Society of Chemistry: London, UK, 2013; Volume 39, pp. 181-203.
  3. Bieberich, E. Synthesis, Processing, and Function of N-glycans in N-glycoproteins. In Glycobiology of the Nervous System. Advances in Neurobiology; open in new tab
  4. Yu, R., Schengrund, C.L., Eds.; Springer: New York, NY, USA, 2014; Volume 9, pp. 47-70.
  5. Heightman, T.D.; Vasella, A.T. Recent Insights into Inhibition, Structure, and Mechanism of Con-figuration-Retaining Glycosidases. Angew. Chem. Int. Ed. 1999, 38, 750-770. [CrossRef] Molecules 2020, 25, 5942 open in new tab
  6. Krasikov, V.V.; Karelov, D.V.; Firsov, L.M. α-Glucosidases. Biochemistry 2001, 66, 267-281. open in new tab
  7. Lillelund, V.H.; Jensen, H.H.; Liang, X.; Bols, M. Recent Developments of Transition-State Analogue Glycosidase Inhibitors of Non-Natural Product Origin. Chem. Rev. 2002, 102, 515-554. [CrossRef] [PubMed] open in new tab
  8. Legler, G. Glycoside Hydrolases: Mechanistic Information from Studies with Reversible and Irre-versible Inhibitors. Adv. Carbohydr. Chem. Biochem. 1990, 48, 319-384. [PubMed] open in new tab
  9. Chiba, S. Molecular Mechanism in α-Glucosidase and Glucoamylase. Biosci. Biotechnol. Biochem. 1997, 61, 1233-1239. [CrossRef] [PubMed] open in new tab
  10. Piszkiewicz, D.; Bruice, T.C. Glycoside Hydrolysis. II. Intramolecular Carboxyl and Acetamido Group Catalysis in β-Glycoside Hydrolysis. J. Am. Chem. Soc. 1968, 90, 2156-2163. [CrossRef] open in new tab
  11. Bauer, M.W.; Bylina, E.J.; Swanson, R.V.; Kelly, R.M. Comparison of a β-Glucosidase and a β-Mannosidase from the Hyperthermophilic ArchaeonPyrococcus furiosus. J. Biol. Chem. 1996, 271, 23749-23755. [CrossRef] open in new tab
  12. Mahapatra, S.; Vickram, A.S.; Sridharan, T.B.; Parameswari, R.; Pathy, M.R. Screening, production, optimization and characterization of β-glucosidase using microbes from shellfish waste. 3 Biotech 2016, 6, 213. [CrossRef] open in new tab
  13. Zhang, S.; Xie, J.; Zhao, L.; Pei, J.; Su, E.; Xiao, W.; Wang, Z. Cloning, overexpression and character-ization of a thermostable β-xylosidase from Thermotoga petrophila and cooperated transformation of ginsenoside extract to ginsenoside 20(S)-Rg3 with a β-glucosidase. Bioorg. Chem. 2019, 85, 159-167. [CrossRef] open in new tab
  14. Tiwari, P.; Misra, B.N.; Sangwan, N.S. β-Glucosidases from the FungusTrichoderma: An Efficient Cellulase Machinery in Biotechnological Applications. BioMed Res. Int. 2013, 2013, 1-10. [CrossRef] [PubMed] open in new tab
  15. Sørensen, A.; Lübeck, M.; Lubeck, P.S.; Ahring, B.K. Fungal Beta-Glucosidases: A Bottleneck in Industrial Use of Lignocellulosic Materials. Biomolecules 2013, 3, 612-631. [CrossRef] open in new tab
  16. Del Cueto, J.; Møller, B.L.; Dicenta, F.; Sánchez-Pérez, R. β-Glucosidase activity in almond seeds. Plant Physiol. Biochem. 2018, 126, 163-172. [CrossRef] [PubMed] open in new tab
  17. Li, Y.-K.; Chang, L.-F.; Shu, H.-H.; Chir, J. Characterization of an Isozyme of β-Glucosidase from Sweet Almond. J. Chin. Chem. Soc. 1997, 44, 81-87. [CrossRef] open in new tab
  18. Cicek, M.; Esen, A. Structure and Expression of a Dhurrinase (β-Glucosidase) from Sorghum. Plant Physiol. 1998, 116, 1469-1478. [CrossRef] open in new tab
  19. Pankoke, H.; Buschmann, T.; Müller, C. Role of plant β-glucosidases in the dual defense system of iridoid glycosides and their hydrolyzing enzymes in Plantago lanceolata and Plantago major. Phytochemistry 2013, 94, 99-107. [CrossRef] open in new tab
  20. Barrett, T.; Suresh, C.G.; Tolley, S.P.; Dodson, E.J.; Hughes, M.A. The crystal structure of a cyanogenic β-glucosidase from white clover, a family 1 glycosyl hydrolase. Structure 1995, 3, 951-960. [CrossRef] open in new tab
  21. Ioku, K.; Pongpiriyadacha, Y.; Konishi, Y.; Takei, Y.; Nakatani, N.; Terao, J. β-Glucosidase Activity in the Rat Small Intestine toward Quercetin Monoglucosides. Biosci. Biotechnol. Biochem. 1998, 62, 1428-1431. [CrossRef] open in new tab
  22. Raychaudhuri, C.; Desai, I.D. Lysosomal β-glucosidase and β-xylosidase of rat intestine. Int. J. Biochem. 1972, 3, 684-690. [CrossRef] open in new tab
  23. Gopalan, V.; Vander Jagt, D.J.; Libell, D.P.; Glew, R.H. Transglucosylation as a probe of the mecha-nism of action of mammalian cytosolic β-glucosidase. J. Biol. Chem. 1992, 267, 9629-9638.
  24. Philip, J.S.; Gilbert, H.J.; Smithard, R.R. Growth, viscosity and beta-glucanase activity of intestinal fluid in broiler chickens fed on barley-based diets with or without exogenous beta-glucanase. Br. Poult. Sci. 1995, 36, 599-603. [CrossRef] [PubMed] open in new tab
  25. Lelieveld, L.T.; Mirzaian, M.; Kuo, C.L.; Artola, M.; Ferraz, M.J.; Peter, R.E.A.; Akiyama, H.; Greimel, P.; Van den Berg, R.J.B.H.N.; Overkleeft, H.S.; et al. Role of β-glucosidase 2 in aberrant glycosphin-golipid metabolism: Model of glucocerebrosidase deficiency in zebrafish. J. Lipid Res. 2019, 60, 1851-1867. [CrossRef] [PubMed] open in new tab
  26. Yeoman, C.J.; Han, Y.; Dodd, D.; Schroeder, C.M.; Mackie, R.I.; Cann, I.K.O. Thermostable enzymes as biocatalysts in the biofuel industry. Adv. Appl. Microbiol. 2010, 70, 1-55. [PubMed] open in new tab
  27. Asati, V.; Sharma, P.K. Purification and characterization of an isoflavones conjugate hydrolyzing β-glucosidase (ICHG) from Cyamopsis tetragonoloba (guar). Biochem. Biophys. Rep. 2019, 20, 100669. [CrossRef] [PubMed] open in new tab
  28. Amiri, B.; Hosseini, N.S.; Taktaz, F.; Amini, K.; Rahmani, M.; Amiri, M.; Sadrjavadi, K.; Jangholi, A.; Esmaeili, S. Inhibitory effects of selected antibiotics on the activities of α-amylase and α-glucosidase: In-vitro, in-vivo and theoretical studies. Eur. J. Pharm. Sci. 2019, 138, 105040. [CrossRef] open in new tab
  29. Martínez-Bailén, M.; Jiménez-Ortega, E.; Carmona, A.T.; Robina, I.; Sanz-Aparicio, J.; Talens-Perales, D.; Polaina, J.; Matassini, C.; Cardona, F.; Moreno-Vargas, A.J. Structural basis of the inhibition of GH1 β-glucosidases by multivalent pyrrolidine iminosugars. Bioorg. Chem. 2019, 89, 103026. [CrossRef] open in new tab
  30. Durantel, D.; Alotte, C.; Zoulim, F. Glucosidase inhibitors as antiviral agents for hepatitis B and C. Curr. Opin. Investig. 2007, 8, 125-129.
  31. Pandey, S.; Sree, A.; Dash, S.S.; Sethi, D.P.; Chowdhury, L. Diversity of marine bacteria producing beta-glucosidase inhibitors. Microb. Cell Fact. 2013, 12, 35. [CrossRef] open in new tab
  32. Puls, W.; Keup, U.; Krause, H.P.; Thomas, G.; Hoffmeister, F. Glucosidase inhibition-A new approach to the treatment of diabetes, obesity, and hyperlipoproteinaemia. Naturwissenschaften 1977, 64, 536-537. [CrossRef] open in new tab
  33. Brogard, J.M.; Willemin, B.; Blicklé, J.F.; Lamalle, A.M.; Stahl, A. Inhibiteurs des alpha-glucosidases: Une nouvelle approche thérapeutique du diabète et des hypoglycémies fonctionnelles. Rev. Med. Intern. 1989, 10, 365-374. [CrossRef] open in new tab
  34. Lankatillake, C.; Huynh, T.; Dias, D.A. Understanding glycaemic control and current approaches for screening antidiabetic natural products from evidence-based medicinal plants. Plant Methods 2019, 15, 1-35. [CrossRef] open in new tab
  35. Teng, H.; Chen, L.; Fang, T.; Yuan, B.; Lin, Q. Rb2 inhibits α-glucosidase and regulates glucose me-tabolism by activating AMPK pathways in HepG2 cells. J. Funct. Foods 2017, 28, 306-313. [CrossRef] open in new tab
  36. Kato, A.; Kato, N.; Kano, E.; Adachi, I.; Ikeda, K.; Yu, L.; Okamoto, T.; Banba, Y.; Ouchi, H.; Takahata, H.; et al. Biological properties of D-and L-1-deoxyazasugars. J. Med. Chem. 2005, 48, 2036-2044. [CrossRef] [PubMed] open in new tab
  37. Fan, J.-Q.; Ishii, S.; Asano, N.; Suzuki, Y. Accelerated transport and maturation of lysosomal α-galactosidase A in Fabry lymphoblasts by an enzyme inhibitor. Nat. Med. 1999, 5, 112-115. [CrossRef] [PubMed] open in new tab
  38. Sawkar, A.R.; Cheng, W.C.; Beutler, E.; Wong, C.H.; Balch, W.E.; Kelly, J.W. Chemical chaperones increase the cellular activity of N370S β-glucosidase: A therapeutic strategy for Gaucher disease. Proc. Natl. Acad. Sci. USA 2002, 99, 15428-15433. [CrossRef] [PubMed] open in new tab
  39. Gaulton, A.; Bellis, L.J.; Bento, A.P.; Chambers, J.; Davies, M.; Hersey, A.; Light, Y.; McGlinchey, S.; Michalovich, D.; Al-Lazikani, B.; et al. ChEMBL: A large-scale bioactivity database for drug discovery. Nucleic Acids Res. 2012, 40, D1100-D1107. [CrossRef] open in new tab
  40. Bender, A. Databases: Compound bioactivities go public. Nat. Chem. Biol. 2010, 6, 309. [CrossRef] open in new tab
  41. Kim, S.; Thiessen, P.A.; Bolton, E.E.; Chen, J.; Fu, G.; Gindulyte, A.; Han, L.; He, J.; He, S.; Shoemaker, B.A.; et al. PubChem Substance and Compound databases. Nucleic Acids Res. 2016, 44, D1202-D1213. [CrossRef] open in new tab
  42. Toropov, A.A.; Toropova, A.P.; Raitano, G.; Benfenati, E. CORAL: Building up QSAR models for the chromosome aberration test. Saudi J. Biol. Sci. 2019, 26, 1101-1106. [CrossRef] open in new tab
  43. Ahmadi, S.; Ghanbari, H.; Lotfi, S.; Azimi, N. Predictive QSAR modeling for the antioxidant activity of natural compounds derivatives based on Monte Carlo method. Mol. Divers. 2020, 1-11. [CrossRef] open in new tab
  44. Przybyłek, M.; Jeliński, T.; Słabuszewska, J.; Ziółkowska, D.; Mroczyńska, K.; Cysewski, P. Application of Multivariate Adaptive Regression Splines (MARSplines) Methodology for Screening of Di-carboxylic Acid Cocrystal Using 1D and 2D Molecular Descriptors. Cryst. Growth Des. 2019, 19, 3876-3887. [CrossRef] open in new tab
  45. Sundar, K.; Rosy, J.C.; Balamurali, S.; Mary, J.A.; Shenbagara, R. Generation of 2D-QSAR Model for Angiogenin Inhibitors: A Ligand-Based Approach for Cancer Drug Design. Trends Bioinform. 2016, 9, 1-13. [CrossRef] open in new tab
  46. Toropov, A.A.; Toropova, A.P.; Veselinović, A.M.; Leszczynska, D.; Leszczynski, J. SARS-CoV Mpro inhibitory activity of aromatic disulfide compounds: QSAR model. J. Biomol. Struct. Dyn. 2020. [CrossRef] open in new tab
  47. Tran, T.-S.; Le, M.-T.; Tran, T.-D.; Tran, T.-H.; Thai, K.-M. Design of Curcumin and Flavonoid Derivatives with Acetylcholinesterase and Beta-Secretase Inhibitory Activities Using in Silico Approaches. Molecules 2020, 25, 3644. [CrossRef] [PubMed] open in new tab
  48. Przybyłek, M.; Cysewski, P. Distinguishing Cocrystals from Simple Eutectic Mixtures: Phenolic Acids as Potential Pharmaceutical Coformers. Cryst. Growth Des. 2018, 18, 3524-3534. [CrossRef] open in new tab
  49. Dieguez-Santana, K.; Pham-The, H.; Rivera-Borroto, O.M.; Puris, A.; Le-Thi-Thu, H.; Casanola-Martin, G.M. A Two QSAR Way for Antidiabetic Agents Targeting Using α-Amylase and α-Glucosidase In-hibitors: Model Parameters Settings in Artificial Intelligence Techniques. Lett. Drug Des. Discov. 2017, 14. [CrossRef] open in new tab
  50. Taxak, N.; Bharatam, P.V. 2D QSAR study for gemfibrozil glucuronide as the mechanism-based in-hibitor of CYP2C8. Indian J. Pharm. Sci. 2013, 75, 680-687. open in new tab
  51. Jafari, K.; Fatemi, M.H.; Toropova, A.P.; Toropov, A.A. Correlation Intensity Index (CII) as a criterion of predictive potential: Applying to model thermal conductivity of metal oxide-based ethylene glycol nanofluids. Chem. Phys. Lett. 2020, 754, 137614. [CrossRef] open in new tab
  52. Yap, C.W. PaDEL-descriptor: An open source software to calculate molecular descriptors and fin-gerprints. J. Comput. Chem. 2011, 32, 1466-1474. [CrossRef] open in new tab
  53. Lei, T.; Li, Y.; Song, Y.; Li, D.; Sun, H.; Hou, T.-J. ADMET evaluation in drug discovery: 15. Accurate prediction of rat oral acute toxicity using relevance vector machine and consensus modeling. J. Cheminform. 2016, 8, 1-19. [CrossRef] open in new tab
  54. Goodarzi, M.; Dejaegher, B.; Heyden, Y. Vander Feature selection methods in QSAR studies. J. AOAC Int. 2012, 95, 636-651. [CrossRef] [PubMed] open in new tab
  55. Li, Y.; Dai, Z.; Cao, D.; Luo, F.; Chen, Y.; Yuan, Z. Chi-MIC-share: A new feature selection algorithm for quantitative structure-activity relationship models. RSC Adv. 2020, 10, 19852-19860. [CrossRef] open in new tab
  56. Alsenan, S.A.; Al-Turaiki, I.M.; Hafez, A.M. Feature extraction methods in quantitative struc-ture-activity relationship modeling: A comparative study. IEEE Access 2020, 8, 78737-78752. [CrossRef] open in new tab
  57. Newby, D.; Freitas, A.A.; Ghafourian, T. Pre-processing Feature Selection for Improved C&RT Models for Oral Absorption. J. Chem. Inf. Model. 2013, 53, 2730-2742. [CrossRef] [PubMed] open in new tab
  58. Antanasijević, J.; Antanasijević, D.; Pocajt, V.; Trišović, N.; Fodor-Csorba, K. A QSPR study on the liquid crystallinity of five-ring bent-core molecules using decision trees, MARS and artificial neural networks. RSC Adv. 2016, 6, 18452-18464. [CrossRef] open in new tab
  59. Todeschini, R.; Consonni, V. Molecular Descriptors for Chemoinformatics. In Molecular Descriptors for Chemoinformatics; Wiley-VCH: Weinheim, Germany, 2009; Volume 2, ISBN 978-3-527-62876-6. open in new tab
  60. Burden, F.R. Molecular identification number for substructure searches. J. Chem. Inf. Model. 1989, 29, 225-227. [CrossRef] open in new tab
  61. Burden, F.R. A Chemically Intuitive Molecular Index Based on the Eigenvalues of a Modified Adjacency Matrix. Quant. Struct. Relatsh. 1997, 16, 309-314. [CrossRef] open in new tab
  62. Broto, P.; Moreau, G.; Vandycke, C. Molecular structures: Perception, autocorrelation descriptor and sar studies: System of atomic contributions for the calculation of the n-octanol/water partition coef-ficients. Eur. J. Med. Chem. 1984, 19, 71-78.
  63. Broto, P.; Moreau, G.; Vandycke, C. Molecular structures: Perception, autocorrelation descriptor and sar studies: Autocorrelation descriptor. Eur. J. Med. Chem. 1984, 19, 66-70.
  64. Moreau, G.; Broto, P. Autocorrelation of molecular structures. Application to SAR studies. Nouv. J. Chim. 1980, 4, 757-764.
  65. Moreau, J.L.; Broto, P. The autocorrelation of a topologial structure: A new molecular descriptor. Nouv. J. Chim. 1980, 4, 359-360. open in new tab
  66. Huuskonen, J.J.; Livingstone, D.J.; Tetko, I.V. Neural network modeling for estimation of partition coefficient based on atom-type electrotopological state indices. J. Chem. Inf. Comput. Sci. 2000, 40, 947-955. [CrossRef] [PubMed] open in new tab
  67. Huuskonen, J.J.; Villa, A.E.P.; Tetko, I.V. Prediction of partition coefficient based on atom-type electrotopological state indices. J. Pharm. Sci. 1999, 88, 229-233. [CrossRef] [PubMed] open in new tab
  68. Kier, L.B.; Hall, L.H. Molecular Structure Description: The Electrotopological State; Academic Press: London, UK, 1999; ISBN 978-0-12-406555-0.
  69. Kier, L.B.; Hall, L.H. An Electrotopological-State Index for Atoms in Molecules. Pharm. Res. 1990, 7, 801-807. [CrossRef] [PubMed] open in new tab
  70. Kier, L.B.; Hall, L.H.; Frazer, J.W. An index of electrotopological state for atoms in molecules. J. Math. Chem. 1991, 7, 229-241. [CrossRef] open in new tab
  71. Votano, J.R.; Parham, M.; Hall, L.H.; Kier, L.B.; Oloff, S.; Tropsha, A.; Xie, Q.; Tong, W. Three new consensus QSAR models for the prediction of Ames genotoxicity. Mutagenesis 2004, 19, 365-377. [CrossRef] open in new tab
  72. Fjodorova, N.; Vračko, M.; Novič, M.; Roncaglioni, A.; Benfenati, E. New public QSAR model for carcinogenicity. Chem. Cent. J. 2010, 4, S3. [CrossRef] open in new tab
  73. Parmeggiani, C.; Catarzi, S.; Matassini, C.; D'Adamio, G.; Morrone, A.; Goti, A.; Paoli, P.; Cardona, F. Human Acid β-Glucosidase Inhibition by Carbohydrate Derived Iminosugars: Towards New Pharmacological Chaperones for Gaucher Disease. ChemBioChem 2015, 16, 2054-2064. [CrossRef] open in new tab
  74. Yamashita, T.; Yasuda, K.; Kizu, H.; Kameda, Y.; Watson, A.A.; Nash, R.J.; Fleet, G.W.J.; Asano, N. New polyhydroxylated pyrrolidine, piperidine, and pyrrolizidine alkaloids from Scilla sibirica. J. Nat. Prod. 2002, 65, 1875-1881. [CrossRef] open in new tab
  75. Matthews, B.W. Comparison of the predicted and observed secondary structure of T4 phage lyso-zyme. BBA Protein Struct. 1975, 405, 442-451. [CrossRef] open in new tab
  76. Boughorbel, S.; Jarray, F.; El-Anbari, M. Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric. PLoS ONE 2017, 12, e0177678. [CrossRef] [PubMed] open in new tab
  77. Paul, S.; Arlehamn, C.S.L.; Schulten, V.; Westernberg, L.; Sidney, J.; Peters, B.; Sette, A. Experimental validation of the RATE tool for inferring HLA restrictions of T cell epitopes. BMC Immunol. 2017, 18, 20. [CrossRef] [PubMed] open in new tab
  78. Klingspohn, W.; Mathea, M.; Ter Laak, A.; Heinrich, N.; Baumann, K. Efficiency of different measures for defining the applicability domain of classification models. J. Cheminform. 2017, 9, 1-17. [CrossRef] [PubMed] open in new tab
  79. Cai, C.; Fang, J.; Guo, P.; Wang, Q.; Hong, H.; Moslehi, J.; Cheng, F. In Silico Pharmacoepidemiologic Evaluation of Drug-Induced Cardiovascular Complications Using Combined Classifiers. J. Chem. Inf. Model. 2018, 58, 943-956. [CrossRef] [PubMed] open in new tab
  80. Davis, J.; Goadrich, M. The relationship between precision-recall and ROC curves. ACM Int. Conf. Proc. Ser. 2006, 148, 233-240. open in new tab
  81. Bradley, A.P. The use of the area under the ROC curve in the evaluation of machine learning algo-rithms. Pattern Recognit. 1997, 30, 1145-1159. [CrossRef] open in new tab
  82. Brown, J. Classifiers and their Metrics Quantified. Mol. Inform. 2018, 37, 1700127. [CrossRef] open in new tab
  83. Chicco, D.; Jurman, G. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genom. 2020, 21, 1-13. [CrossRef] open in new tab
  84. Halimu, C.; Kasem, A.; Newaz, S.H.S. Empirical comparison of area under ROC curve (AUC) and Mathew correlation coefficient (MCC) for evaluating machine learning algorithms on imbalanced datasets for binary classification. ACM Int. Conf. Proc. Ser. 2019, 1-6. [CrossRef] open in new tab
  85. Lobo, J.M.; Jiménez-valverde, A.; Real, R. AUC: A misleading measure of the performance of pre-dictive distribution models. Glob. Ecol. Biogeogr. 2008, 17, 145-151. [CrossRef] open in new tab
  86. Muschelli, J. ROC and AUC with a Binary Predictor: A Potentially Misleading Metric. J. Classif. 2020, 37, 696-708. [CrossRef] [PubMed] open in new tab
  87. Kovalishyn, V.; Aires-de-Sousa, J.; Ventura, C.; Leitão, E.R.; Martins, F. QSAR modeling of an-titubercular activity of diverse organic compounds. Chemom. Intell. Lab. Syst. 2011, 107, 69-74. [CrossRef] open in new tab
  88. Tropsha, A.; Gramatica, P.; Gombar, V.K. The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models. QSAR Comb. Sci. 2003, 22, 69-77. [CrossRef] open in new tab
  89. Puzyn, T.; Mostrag-Szlichtyng, A.; Gajewicz-Skretna, A.; Skrzyński, M.; Worth, A. Investigating the influence of data splitting on the predictive ability of QSAR/QSPR models. Struct. Chem. 2011, 22, 795-804. [CrossRef] open in new tab
  90. Pilón-Jiménez, B.A.; Saldívar-González, F.I.; Díaz-Eufracio, B.I.; Medina-Franco, J.L. BIOFACQUIM: A Mexican Compound Database of Natural Products. Biomolecules 2019, 9, 31. [CrossRef] open in new tab
  91. Nikitina, A.; Orlov, A.; Kozlovskaya, L.; Palyulin, V.; Osolodkin, D.I. Enhanced taxonomy annotation of antiviral activity data from ChEMBL. Database 2019, 2019, 1-18. [CrossRef] open in new tab
  92. Haudecoeur, R.; Peuchmaur, M.; Ahmed-Belkacem, A.; Pawlotsky, J.M.; Boumendjel, A. Structure-activity relationships in the development of allosteric hepatitis C virus RNA-dependent RNA polymerase inhibitors: Ten years of research. Med. Res. Rev. 2013, 33, 934-984. [CrossRef] open in new tab
  93. Bankar, A.; Siriwardena, T.P.; Rizoska, B.; Rydergård, C.; Kylefjord, H.; Rraklli, V.; Eneroth, A.; Pinho, P.; Norin, S.; Bylund, J.; et al. 5-Fluorotroxacitabine Displays Potent Anti-Leukemic Effects and Circumvents Resistance to Ara-C. Blood 2018, 132, 3939. [CrossRef] open in new tab
  94. Szilágyi, K.; Hajdú, I.; Flachner, B.; Lőrincz, Z.; Balczer, J.; Gál, P.; Závodszky, P.; Pirli, C.; Balogh, B.; Mándity, I.M.; et al. Design and Selection of Novel C1s Inhibitors by In Silico and In Vitro Approaches. Molecules 2019, 24, 3641. [CrossRef] open in new tab
Verified by:
No verification

seen 18 times

Recommended for you

Meta Tags