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.
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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
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- 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
- 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.
- 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
- Yu, R., Schengrund, C.L., Eds.; Springer: New York, NY, USA, 2014; Volume 9, pp. 47-70.
- 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
- Krasikov, V.V.; Karelov, D.V.; Firsov, L.M. α-Glucosidases. Biochemistry 2001, 66, 267-281. open in new tab
- 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
- 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
- Chiba, S. Molecular Mechanism in α-Glucosidase and Glucoamylase. Biosci. Biotechnol. Biochem. 1997, 61, 1233-1239. [CrossRef] [PubMed] open in new tab
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- Durantel, D.; Alotte, C.; Zoulim, F. Glucosidase inhibitors as antiviral agents for hepatitis B and C. Curr. Opin. Investig. 2007, 8, 125-129.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Bender, A. Databases: Compound bioactivities go public. Nat. Chem. Biol. 2010, 6, 309. [CrossRef] open in new tab
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Burden, F.R. Molecular identification number for substructure searches. J. Chem. Inf. Model. 1989, 29, 225-227. [CrossRef] open in new tab
- 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
- 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.
- Broto, P.; Moreau, G.; Vandycke, C. Molecular structures: Perception, autocorrelation descriptor and sar studies: Autocorrelation descriptor. Eur. J. Med. Chem. 1984, 19, 66-70.
- Moreau, G.; Broto, P. Autocorrelation of molecular structures. Application to SAR studies. Nouv. J. Chim. 1980, 4, 757-764.
- 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
- 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
- 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
- Kier, L.B.; Hall, L.H. Molecular Structure Description: The Electrotopological State; Academic Press: London, UK, 1999; ISBN 978-0-12-406555-0.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Brown, J. Classifiers and their Metrics Quantified. Mol. Inform. 2018, 37, 1700127. [CrossRef] open in new tab
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
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