{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:07:59Z","timestamp":1767337679251,"version":"build-2065373602"},"reference-count":91,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2019,10,30]],"date-time":"2019-10-30T00:00:00Z","timestamp":1572393600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["UID\/QUI\/50006\/2019"],"award-info":[{"award-number":["UID\/QUI\/50006\/2019"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Molecules"],"abstract":"<jats:p>Two isoforms of extracellular regulated kinase (ERK), namely ERK-1 and ERK-2, are associated with several cellular processes, the aberration of which leads to cancer. The ERK-1\/2 inhibitors are thus considered as potential agents for cancer therapy. Multitarget quantitative structure\u2013activity relationship (mt-QSAR) models based on the Box\u2013Jenkins approach were developed with a dataset containing 6400 ERK inhibitors assayed under different experimental conditions. The first mt-QSAR linear model was built with linear discriminant analysis (LDA) and provided information regarding the structural requirements for better activity. This linear model was also utilised for a fragment analysis to estimate the contributions of ring fragments towards ERK inhibition. Then, the random forest (RF) technique was employed to produce highly predictive non-linear mt-QSAR models, which were used for screening the Asinex kinase library and identify the most potential virtual hits. The fragment analysis results justified the selection of the hits retrieved through such virtual screening. The latter were subsequently subjected to molecular docking and molecular dynamics simulations to understand their possible interactions with ERK enzymes. The present work, which utilises in-silico techniques such as multitarget chemometric modelling, fragment analysis, virtual screening, molecular docking and dynamics, may provide important guidelines to facilitate the discovery of novel ERK inhibitors.<\/jats:p>","DOI":"10.3390\/molecules24213909","type":"journal-article","created":{"date-parts":[[2019,10,31]],"date-time":"2019-10-31T05:18:26Z","timestamp":1572499106000},"page":"3909","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Multi-Target Chemometric Modelling, Fragment Analysis and Virtual Screening with ERK Inhibitors as Potential Anticancer Agents"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4818-9047","authenticated-orcid":false,"given":"Amit Kumar","family":"Halder","sequence":"first","affiliation":[{"name":"LAQV@REQUIMTE\/Department of Chemistry and Biochemistry, University of Porto, 4169-007 Porto, Portugal"}]},{"given":"Amal Kanta","family":"Giri","sequence":"additional","affiliation":[{"name":"LAQV@REQUIMTE\/Department of Chemistry and Biochemistry, University of Porto, 4169-007 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3375-8670","authenticated-orcid":false,"given":"Maria Nata\u0301lia Dias Soeiro","family":"Cordeiro","sequence":"additional","affiliation":[{"name":"LAQV@REQUIMTE\/Department of Chemistry and Biochemistry, University of Porto, 4169-007 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Cicenas, J., Zalyte, E., Rimkus, A., Dapkus, D., Noreika, R., and Urbonavicius, S. (2017). JNK, p38, ERK, and SGK1 Inhibitors in Cancer. Cancers, 10.","DOI":"10.3390\/cancers10010001"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"726","DOI":"10.1096\/fasebj.9.9.7601337","article-title":"The MAPK signaling cascade","volume":"9","author":"Seger","year":"1995","journal-title":"FASEB"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1038\/nrc2109","article-title":"Hyperactive Ras in developmental disorders and cancer","volume":"7","author":"Schubbert","year":"2007","journal-title":"Nat. Rev. Cancer"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.phrs.2012.04.005","article-title":"ERK1\/2 MAP kinases: Structure, function, and regulation","volume":"66","author":"Roskoski","year":"2012","journal-title":"Pharmacol. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1158\/2159-8290.CD-17-1355","article-title":"ERK inhibition: A new front in the war against MAPK pathway-driven cancers?","volume":"8","author":"Smalley","year":"2018","journal-title":"Cancer Disc."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1143","DOI":"10.1158\/1535-7163.MCT-11-1010","article-title":"ERK inhibition overcomes acquired resistance to MEK inhibitors","volume":"11","author":"Hatzivassiliou","year":"2012","journal-title":"Mol. Cancer Ther."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1016\/j.molonc.2014.01.003","article-title":"Differential activity of MEK and ERK inhibitors in BRAF inhibitor resistant melanoma","volume":"8","author":"Carlino","year":"2014","journal-title":"Mol. Oncol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1016\/j.apsb.2018.01.008","article-title":"Targeting ERK, an Achilles\u2019 heel of the MAPK pathway, in cancer therapy","volume":"8","author":"Liu","year":"2018","journal-title":"Acta Pharm. Sin. B"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.pharmthera.2018.02.007","article-title":"ERK1\/2 inhibitors: New weapons to inhibit the RAS-regulated RAF-MEK1\/2-ERK1\/2 pathway","volume":"187","author":"Kidger","year":"2018","journal-title":"Pharmacol. Ther."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"9721","DOI":"10.1128\/JVI.00695-12","article-title":"Distinct roles for extracellular signal-regulated kinase 1 (ERK1) and ERK2 in the structure and production of a primate gammaherpesvirus","volume":"86","author":"Woodson","year":"2012","journal-title":"J. Vir."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2419","DOI":"10.1039\/C8SC00043C","article-title":"Isoform-selective activity-based profiling of ERK signaling","volume":"9","author":"Shin","year":"2018","journal-title":"Chem. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"53","DOI":"10.3389\/fcell.2016.00053","article-title":"ERK1 and ERK2 map kinases: Specific roles or functional redundancy?","volume":"4","author":"Busca","year":"2016","journal-title":"Front. Cell Dev. Biol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4044","DOI":"10.1158\/1078-0432.CCR-17-3674","article-title":"ERK mutations and amplification confer resistance to ERK-inhibitor therapy","volume":"24","author":"Jaiswal","year":"2018","journal-title":"Clin. Cancer Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"576","DOI":"10.3324\/haematol.2018.196931","article-title":"Mutations in the RAS-BRAF-MAPK-ERK pathway define a specific subgroup of patients with adverse clinical features and provide new therapeutic options in chronic lymphocytic leukemia","volume":"104","author":"Gimenez","year":"2019","journal-title":"Haematologica"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.phrs.2019.01.039","article-title":"Targeting ERK1\/2 protein-serine\/threonine kinases in human cancers","volume":"142","author":"Roskoski","year":"2019","journal-title":"Pharmacol. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1158\/2159-8290.CD-17-1119","article-title":"First-in-class ERK1\/2 inhibitor ulixertinib (BVD-523) in patients with MAPK mutant advanced solid tumors: Results of a phase I dose-escalation and expansion study","volume":"8","author":"Sullivan","year":"2018","journal-title":"Cancer Discov."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.canlet.2019.05.025","article-title":"AKT and ERK dual inhibitors: The way forward?","volume":"459","author":"Cao","year":"2019","journal-title":"Cancer Lett."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1038\/nrd1608","article-title":"Data integration: Challenges for drug discovery","volume":"4","author":"Searls","year":"2005","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1538","DOI":"10.1016\/j.drudis.2018.05.010","article-title":"Machine learning in chemoinformatics and drug discovery","volume":"23","author":"Lo","year":"2018","journal-title":"Drug Discov. Today"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2021","DOI":"10.4155\/fmc-2018-0213","article-title":"Recent advances in fragment-based computational drug design: Tackling simultaneous targets\/biological effects","volume":"10","year":"2018","journal-title":"Futur. Med. Chem."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jmgm.2014.07.001","article-title":"In silico screening and study of novel ERK2 inhibitors using 3D QSAR, docking and molecular dynamics","volume":"53","author":"Larif","year":"2014","journal-title":"J. Mol. Graph. Model."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4900","DOI":"10.1016\/j.bmcl.2011.06.016","article-title":"Structure tuning of pyrazolylpyrrole derivatives as ERK inhibitors utilizing dual tools; 3D-QSAR and side-chain hopping","volume":"21","author":"Kim","year":"2011","journal-title":"Bioorganic Med. Chem. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1280","DOI":"10.1021\/jm061381f","article-title":"Flipped out: Structure-guided design of selective pyrazolylpyrrole ERK inhibitors","volume":"50","author":"Aronov","year":"2007","journal-title":"J. Med. Chem."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1635","DOI":"10.2174\/092986712799945058","article-title":"Role of ligand-based drug design methodologies toward the discovery of new anti-Alzheimer agents: Futures perspectives in fragment-based ligand design","volume":"19","author":"Luan","year":"2012","journal-title":"Curr. Med. Chem."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"6239","DOI":"10.1016\/j.bmc.2011.09.015","article-title":"Multi-target drug discovery in anti-cancer therapy: Fragment-based approach toward the design of potent and versatile anti-prostate cancer agents","volume":"19","author":"Kleandrova","year":"2011","journal-title":"Bioorganic Med. Chem."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5910","DOI":"10.1016\/j.ejmech.2011.09.055","article-title":"Fragment-based QSAR model toward the selection of versatile anti-sarcoma leads","volume":"46","author":"Kleandrova","year":"2011","journal-title":"Eur. J. Med. Chem."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1007\/s11030-017-9731-1","article-title":"Fragment-based in silico modeling of multi-target inhibitors against breast cancer-related proteins","volume":"21","author":"Cordeiro","year":"2017","journal-title":"Mol. Div."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Halder, A.K., and Cordeiro, M.N.D.S. (2019). Development of multi-target chemometric models for the inhibition of class I PI3K enzyme isoforms: A case study using QSAR-Co tool. Int. J. Mol. Sci., 20.","DOI":"10.3390\/ijms20174191"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1016\/j.chembiol.2003.09.002","article-title":"The process of structure-based drug design","volume":"10","author":"Anderson","year":"2003","journal-title":"Chem. Biol."},{"key":"ref_30","unstructured":"(2019, August 30). Software QUBILs-MAS v1.0. Available online: http:\/\/tomocomd.com\/qubils-mas."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1186\/s13321-017-0211-5","article-title":"QuBiLS-MAS, open source multi-platform software for atom- and bond-based topological (2D) and chiral (2.5D) algebraic molecular descriptors computations","volume":"9","author":"Barigye","year":"2017","journal-title":"J. Cheminform."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"943","DOI":"10.1080\/1062936X.2015.1104517","article-title":"QuBiLs-MAS method in early drug discovery and rational drug identification of antifungal agents","volume":"26","author":"Barigye","year":"2015","journal-title":"SAR QSAR Environ. Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"14704","DOI":"10.1021\/acsomega.8b02419","article-title":"Combining ensemble learning with a fragment-based topological approach to generate new molecular diversity in drug discovery: In silico design of Hsp90 inhibitors","volume":"3","year":"2018","journal-title":"ACS Omega"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1007\/s11030-018-9890-8","article-title":"BET bromodomain inhibitors: Fragment-based in silico design using multi-target QSAR models","volume":"23","author":"Scotti","year":"2019","journal-title":"Mol. Div."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2345","DOI":"10.1007\/s00044-017-1936-4","article-title":"De novo computational design of compounds virtually displaying potent antibacterial activity and desirable in vitro ADMET profiles","volume":"26","author":"Cordeiro","year":"2017","journal-title":"Med. Chem. Res."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.chemolab.2015.07.007","article-title":"\u201cNanoBRIDGES\u201d software: Open access tools to perform QSAR and nano-QSAR modeling","volume":"147","author":"Ambure","year":"2015","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"854","DOI":"10.1021\/ci00020a020","article-title":"Application of genetic function approximation to quantitative structure-activity-relationships and quantitative structure-property relationships","volume":"34","author":"Rogers","year":"1994","journal-title":"J. Chem. Inf. Comp. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2538","DOI":"10.1021\/acs.jcim.9b00295","article-title":"QSAR-Co: An open source software for developing robust multitasking or multitarget classification-based QSAR models","volume":"59","author":"Ambure","year":"2019","journal-title":"J. Chem. Inf. Model."},{"key":"ref_39","unstructured":"(2019, August 04). QSAR-Co Tool. Available online: https:\/\/sites.google.com\/view\/qsar-co."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Tinsley, H.E.A., and Brown, S.D. (2000). 11-Cluster Analysis. Handbook of Applied Multivariate Statistics and Mathematical Modeling, Academic Press.","DOI":"10.1016\/B978-012691360-6\/50002-1"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.chemolab.2015.09.011","article-title":"QSAR models and scaffold-based analysis of non-nucleoside HIV RT inhibitors","volume":"148","author":"Nizami","year":"2015","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"911","DOI":"10.1080\/1062936X.2018.1529702","article-title":"Finding the structural requirements of diverse HIV-1 protease inhibitors using multiple QSAR modelling for lead identification","volume":"29","author":"Halder","year":"2018","journal-title":"SAR QSAR Environ. Res."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1093\/biomet\/24.3-4.471","article-title":"Certain generalizations in the analysis of variance","volume":"24","author":"Wilks","year":"1932","journal-title":"Biometrika"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Tinsley, H.E.A., and Brown, S.D. (2000). 8-Discriminant Analysis. Handbook of Applied Multivariate Statistics and Mathematical Modeling, Academic Press.","DOI":"10.1016\/B978-012691360-6\/50002-1"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Boughorbel, S., Jarray, F., and El-Anbari, M. (2017). Optimal classifier for imbalanced data using Matthews correlation coefficient metric. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0177678"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"822","DOI":"10.1093\/bioinformatics\/btq037","article-title":"Small-sample precision of ROC-related estimates","volume":"26","author":"Hanczar","year":"2010","journal-title":"Bioinformatics"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"10649","DOI":"10.1021\/acssuschemeng.9b01306","article-title":"Probing the environmental toxicity of deep eutectic solvents and their components: An in silico modeling approach","volume":"7","author":"Halder","year":"2019","journal-title":"ACS. Sustain. Chem. Eng."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2345","DOI":"10.1021\/ci700157b","article-title":"Y-randomization and its variants in QSPR\/QSAR","volume":"47","author":"Rucker","year":"2007","journal-title":"J. Chem. Inf. Model."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.chemolab.2015.04.013","article-title":"On a simple approach for determining applicability domain of QSAR models","volume":"145","author":"Roy","year":"2015","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1021\/acscombsci.7b00039","article-title":"Speeding up early drug discovery in antiviral research: A fragment-based in silico approach for the design of virtual anti-hepatitis C leads","volume":"19","author":"Cordeiro","year":"2017","journal-title":"ACS Comb. Sci."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1021\/ci00053a005","article-title":"Atomic physicochemical parameters for three-dimensional-structure-directed quantitative structure-activity relationships. 2. Modeling dispersive and hydrophobic interactions","volume":"27","author":"Ghose","year":"1987","journal-title":"J. Chem. Inf. Comp. Sci."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2887","DOI":"10.1021\/jm9602928","article-title":"The properties of known drugs. 1. Molecular frameworks","volume":"39","author":"Bemis","year":"1996","journal-title":"J. Med. Chem."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1007\/s10822-011-9440-2","article-title":"Online chemical modeling environment (OCHEM): Web platform for data storage, model development and publishing of chemical information","volume":"25","author":"Sushko","year":"2011","journal-title":"J. Comput. Aided Mol. Des."},{"key":"ref_54","first-page":"1687","article-title":"Advanced in silico approaches for drug discovery: Mining information from multiple biological and chemical data through mtk- QSBER and pt-QSPR Strategies","volume":"24","author":"Cordeiro","year":"2017","journal-title":"Curr. Med. Chem."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1080\/21642583.2014.956265","article-title":"Random forests: From early developments to recent advancements","volume":"2","author":"Fawagreh","year":"2014","journal-title":"Syst. Sci. Control Eng."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1773","DOI":"10.1021\/acs.jcim.6b00753","article-title":"Comparison of the predictive performance and interpretability of random forest and linear models on benchmark data sets","volume":"57","author":"Palczewska","year":"2017","journal-title":"J. Chem. Inf. Model."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1007\/s10822-008-9240-5","article-title":"On the interpretation and interpretability of quantitative structure-activity relationship models","volume":"22","author":"Guha","year":"2008","journal-title":"J. Comput. Aid. Mol. Des."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Nawar, S., and Mouazen, A.M. (2017). Comparison between random forests, artificial neural networks and gradient boosted machines methods of on-line Vis-NIR spectroscopy measurements of soil total nitrogen and total carbon. Sensors, 17.","DOI":"10.3390\/s17102428"},{"key":"ref_60","unstructured":"(2019, August 30). Asinex Kinase Library. Available online: http:\/\/www.asinex.com\/focus_kinases\/."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1038\/nchembio.1629","article-title":"A unique inhibitor binding site in ERK1\/2 is associated with slow binding kinetics","volume":"10","author":"Chaikuad","year":"2014","journal-title":"Nat. Chem. Biol."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"742","DOI":"10.1158\/2159-8290.CD-13-0070","article-title":"Discovery of a novel ERK inhibitor with activity in models of acquired resistance to BRAF and MEK inhibitors","volume":"3","author":"Morris","year":"2013","journal-title":"Cancer Disc."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1002\/jcc.21334","article-title":"Software News and Update AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading","volume":"31","author":"Trott","year":"2010","journal-title":"J. Comput. Chem."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"2785","DOI":"10.1002\/jcc.21256","article-title":"AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility","volume":"30","author":"Morris","year":"2009","journal-title":"J. Comput. Chem."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"4978","DOI":"10.1021\/acs.jmedchem.8b00421","article-title":"Fragment-based discovery of a potent, orally bioavailable inhibitor that modulates the phosphorylation and catalytic activity of ERK1\/2","volume":"61","author":"Heightman","year":"2018","journal-title":"J. Med. Chem."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"4047","DOI":"10.1016\/j.bmcl.2015.07.093","article-title":"Recent progress on MAP kinase pathway inhibitors","volume":"25","author":"Uehling","year":"2015","journal-title":"Bioorganic Med. Chem. Lett."},{"key":"ref_67","first-page":"237","article-title":"Dragon software: An easy approach to molecular descriptor calculations","volume":"56","author":"Mauri","year":"2006","journal-title":"MATCH Commun. Math. Comput. Chem."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/S0169-409X(00)00129-0","article-title":"Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings","volume":"46","author":"Lipinski","year":"2001","journal-title":"Adv. Drug Deliv. Rev."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1393","DOI":"10.1021\/cn400111n","article-title":"Model for high-throughput screening of multitarget drugs in chemical neurosciences: Synthesis, assay, and theoretic study of rasagiline carbamates","volume":"4","author":"Alonso","year":"2013","journal-title":"ACS Chem. Neurosci."},{"key":"ref_70","unstructured":"(2001). STATISTICA, Statsoft-Team. version 6.0."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1111\/j.1432-1033.1977.tb11885.x","article-title":"The Protein Data Bank. A computer-based archival file for macromolecular structures","volume":"80","author":"Bernstein","year":"1977","journal-title":"Eur. J. Biochem."},{"key":"ref_72","unstructured":"(2019, August 30). Protein Data Bank. Available online: www.rcsb.org."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1753","DOI":"10.1110\/ps.9.9.1753","article-title":"Modeling of loops in protein structures","volume":"9","author":"Fiser","year":"2000","journal-title":"Protein Sci."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1605","DOI":"10.1002\/jcc.20084","article-title":"UCSF chimera\u2014A visualization system for exploratory research and analysis","volume":"25","author":"Pettersen","year":"2004","journal-title":"J. Comput. Chem."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1002\/prot.22102","article-title":"Very fast prediction and rationalization of pKa values for protein-ligand complexes","volume":"73","author":"Bas","year":"2008","journal-title":"Proteins"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Gasteiger, J., and Marsili, M. (1978). New model for calculating atomic charges in molecules. Tetrahedron Lett., 3181\u20133184.","DOI":"10.1016\/S0040-4039(01)94977-9"},{"key":"ref_77","unstructured":"(2019, August 25). Discovery Studio Visualizer 2017 R2. Available online: https:\/\/www.3dsbiovia.com\/products\/."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1021\/ct700301q","article-title":"GROMACS 4: Algorithms for highly efficient, load-balanced, and scalable molecular simulation","volume":"4","author":"Hess","year":"2008","journal-title":"J. Chem. Theory Comput."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/0010-4655(95)00042-E","article-title":"Gromacs\u2014A message-passing parallel molecular-dynamics implementation","volume":"91","author":"Berendsen","year":"1995","journal-title":"Comput. Phys. Commun."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1355","DOI":"10.1107\/S0907444904011679","article-title":"PRODRG: A tool for high-throughput crystallography of protein-ligand complexes","volume":"60","author":"Schuttelkopf","year":"2004","journal-title":"Acta Crystallogr. D"},{"key":"ref_81","unstructured":"(2019, September 06). PRODRG Server. Available online: http:\/\/prodrg1.dyndns.org\/."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"6269","DOI":"10.1021\/j100308a038","article-title":"The missing term in effective pair potentials","volume":"91","author":"Berendsen","year":"1987","journal-title":"J. Phys. Chem."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.molliq.2016.09.089","article-title":"Cluster formation of NaCl in bulk solutions: Arithmetic vs. geometric combination rules","volume":"228","author":"Giri","year":"2017","journal-title":"J. Mol. Liq."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1016\/j.molliq.2018.07.083","article-title":"Structure and kinetics of water in highly confined conditions: A molecular dynamics simulation study","volume":"268","author":"Giri","year":"2018","journal-title":"J. Mol. Liq."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.desal.2019.02.014","article-title":"Salt separation from water using graphene oxide nanochannels: A molecular dynamics simulation study","volume":"460","author":"Giri","year":"2019","journal-title":"Desalination"},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Hockney, R.W., and Eastwood, J.W. (1988). Computer Simulation Using Particles, CRC Press.","DOI":"10.1201\/9781439822050"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"7182","DOI":"10.1063\/1.328693","article-title":"polymorphic transitions in single-crystals\u2014A new molecular-dynamics method","volume":"52","author":"Parrinello","year":"1981","journal-title":"J. Appl. Phys."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"014101","DOI":"10.1063\/1.2408420","article-title":"Canonical sampling through velocity rescaling","volume":"126","author":"Bussi","year":"2007","journal-title":"J. Chem. Phys."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"8577","DOI":"10.1063\/1.470117","article-title":"A Smooth particle mesh Ewald method","volume":"103","author":"Essmann","year":"1995","journal-title":"J. Chem. Phys."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"10089","DOI":"10.1063\/1.464397","article-title":"Particle Mesh Ewald\u2014An N.Log(N) method for Ewald sums in large systems","volume":"98","author":"Darden","year":"1993","journal-title":"J. Chem. Phys."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"1951","DOI":"10.1021\/ci500020m","article-title":"g_mmpbsa\u2014A GROMACS tool for high-throughput MM-PBSA calculations","volume":"54","author":"Kumari","year":"2014","journal-title":"J. Chem. Inf. Model."}],"container-title":["Molecules"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1420-3049\/24\/21\/3909\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:30:30Z","timestamp":1760189430000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1420-3049\/24\/21\/3909"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10,30]]},"references-count":91,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2019,11]]}},"alternative-id":["molecules24213909"],"URL":"https:\/\/doi.org\/10.3390\/molecules24213909","relation":{},"ISSN":["1420-3049"],"issn-type":[{"type":"electronic","value":"1420-3049"}],"subject":[],"published":{"date-parts":[[2019,10,30]]}}}