|
1
|
European Chemical Agency (ECHA): Practical
guide, . How to use and report (Q)SARs. Version 3.1. ECHA;
Helsinki: 2016, https://echa.europa.eu/documents/10162/13655/pg_report_qsars_en.pdfJuly.
2016
|
|
2
|
Launer RL and Wilkinson GN: Robustness in
the strategy of scientific model building. Robustness in Statistics
(1st). Elsevier. 201–236. 1979.
|
|
3
|
Aouidate A, Ghaleb A, Ghamali M, Chtita S,
Ousaa A, Choukrad M, Sbai A, Bouachrine M and Lakhlifi T: QSAR
study and rustic ligand-based virtual screening in a search for
aminooxadiazole derivatives as PIM1 inhibitors. Chem Cent J.
12:322018. View Article : Google Scholar : PubMed/NCBI
|
|
4
|
Lima MNN, Melo-Filho CC, Cassiano GC,
Neves BJ, Alves VM, Braga RC, Cravo PVL, Muratov EN, Calit J,
Bargieri DY, et al: QSAR-driven design and discovery of novel
compounds with antiplasmodial and transmission blocking activities.
Front Pharmacol. 9:1462018. View Article : Google Scholar : PubMed/NCBI
|
|
5
|
Qin L, Zhang X, Chen Y, Mo L, Zeng H and
Liang Y: Predictive QSAR models for the toxicity of disinfection
byproducts. Molecules. 22:16712017. View Article : Google Scholar
|
|
6
|
Sun L, Yang H, Li J, Wang T, Li W, Liu G
and Tang Y: In silico pediction of compounds binding to human
plasma proteins by QSAR models. ChemMedChem. 13:572–581. 2018.
View Article : Google Scholar : PubMed/NCBI
|
|
7
|
Nembri S, Grisoni F, Consonni V and
Todeschini R: In silico prediction of cytochrome P450-drug
interaction: QSARs for CYP3A4 and CYP2C9. Int J Mol Sci.
17:9142016. View Article : Google Scholar
|
|
8
|
Garmpis N, Damaskos C, Garmpi A,
Dimitroulis D, Spartalis E, Margonis GA, Schizas D, Deskou I, Doula
C, Magkouti E, et al: Targeting histone deacetylases in malignant
melanoma: A future therapeutic agent or just great expectations?
Anticancer Res. 37:5355–5362. 2017.PubMed/NCBI
|
|
9
|
Stueven NA, Schlaeger NM, Monte AP, Hwang
SL and Huang CC: A novel stilbene-like compound that inhibits
melanoma growth by regulating melanocyte differentiation and
proliferation. Toxicol Appl Pharmacol. 337:30–38. 2017. View Article : Google Scholar : PubMed/NCBI
|
|
10
|
Marra A, Ferrone CR, Fusciello C,
Scognamiglio G, Ferrone S, Pepe S, Perri F and Sabbatino F:
Translational research in cutaneous melanoma: New therapeutic
perspectives. Anticancer Agents Med Chem. 18:166–181. 2018.
View Article : Google Scholar : PubMed/NCBI
|
|
11
|
Theodosakis N, Micevic G, Langdon CG,
Ventura A, Means R, Stern DF and Bosenberg MW: p90RSK blockade
inhibits dual BRAF and MEK inhibitor-resistant melanoma by
targeting protein synthesis. J Invest Dermatol. 137:2187–2196.
2017. View Article : Google Scholar : PubMed/NCBI
|
|
12
|
Mioc M, Pavel IZ, Ghiulai R, Coricovac DE,
Farcaş C, Mihali CV, Oprean C, Serafim V, Popovici RA, Dehelean CA,
et al: The cytotoxic effects of betulin-conjugated gold
nanoparticles as stable formulations in normal and melanoma cells.
Front Pharmacol. 9:4292018. View Article : Google Scholar : PubMed/NCBI
|
|
13
|
Memorial Sloan Kettering and Cancer
Center: SK-MEL-5: Human Melanoma Cell Line (ATCC HTB 70).
https://www.mskcc.org/research-advantage/support/technology/tangible-material/human-melanoma-cell-line-sk-mel-5August
30–2018
|
|
14
|
Al-Qathama A, Gibbons S and Prieto JM:
Differential modulation of Bax/Bcl-2 ratio and onset of caspase-3/7
activation induced by derivatives of Justicidin B in human melanoma
cells A375. Oncotarget. 8:95999–96012. 2017. View Article : Google Scholar : PubMed/NCBI
|
|
15
|
Carbone C, Martins-Gomes C, Pepe V, Silva
AM, Musumeci T, Puglisi G, Furneri PM and Souto EB: Repurposing
itraconazole to the benefit of skin cancer treatment: A combined
azole-DDAB nanoencapsulation strategy. Colloids Surf B
Biointerfaces. 167:337–344. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
16
|
Al-Sanea MM, Ali Khan MS, Abdelazem AZ,
Lee SH, Mok PL, Gamal M, Shaker ME, Afzal M, Youssif BG and Omar
NN: Synthesis and in vitro antiproliferative activity of new
1-phenyl-3-(4-(pyridin-3-yl)phenyl)urea scaffold-based compounds.
Molecules. 23:2972018. View Article : Google Scholar
|
|
17
|
Plitzko B, Kaweesa EN and Loesgen S: The
natural product mensacarcin induces mitochondrial toxicity and
apoptosis in melanoma cells. J Biol Chem. 292:21102–21116. 2017.
View Article : Google Scholar : PubMed/NCBI
|
|
18
|
Kalliokoski T, Kramer C, Vulpetti A and
Gedeck P: Comparability of mixed IC50 data: A
statistical analysis. PLoS One. 8:e610072013. View Article : Google Scholar : PubMed/NCBI
|
|
19
|
Niu AQ, Xie LJ, Wang H, Zhu B and Wang SQ:
Prediction of selective estrogen receptor beta agonist using open
data and machine learning approach. Drug Des Devel Ther.
10:2323–2331. 2016. View Article : Google Scholar : PubMed/NCBI
|
|
20
|
Datta S and Das S: Near-bayesian support
vector machines for imbalanced data classification with equal or
unequal misclassification costs. Neural Netw. 70:39–52. 2015.
View Article : Google Scholar : PubMed/NCBI
|
|
21
|
Cortez P: Package ‘rminer’: Data Mining
Classification and Regression Methods. Version 1.4.2. https://cran.r-project.org/web/packages/rminer/rminer.pdfSeptember
2–2016
|
|
22
|
R Core Team R, . A Language and
Environment for Statistical Computing. R Foundation for Statistical
Computing; Vienna: 2018
|
|
23
|
Bischl B, Lang M, Kotthoff L, Schiffner J,
Richter J, Studerus E, Casalicchio G and Jones ZM: mlr: Machine
learning in R. J Mach Learn Res. 17:1–5. 2016.
|
|
24
|
Liaw A and Wiener M: Classification and
regression by randomForest. R News. 2:18–22. 2002.
|
|
25
|
Romanski P and Kotthoff L: FSelector:
Selecting Attributes. R package. version 0.31. https://cran.r-project.org/web/packages/FSelector/index.htmlNovember
19–2018
|
|
26
|
Ho TK: Random decision forests. 1. IEEE
Computer Society Press; Washington, DC: pp. 278–282. 1995
|
|
27
|
Breiman L: Random forests. Mach Learn.
45:5–32. 2001. View Article : Google Scholar
|
|
28
|
Svetnik V, Liaw A, Tong C, Culberson JC,
Sheridan RP and Feuston BP: Random forest: A classification and
regression tool for compound classification and QSAR modeling. J
Chem Inf Comput Sci. 43:1947–1958. 2003. View Article : Google Scholar : PubMed/NCBI
|
|
29
|
Natekin A and Knoll A: Gradient boosting
machines, a tutorial. Front Neurorobot. 7:212013. View Article : Google Scholar : PubMed/NCBI
|
|
30
|
He T, Heidemeyer M, Ban F, Cherkasov A and
Ester M: SimBoost: a read-across approach for predicting
drug-target binding affinities using gradient boosting machines. J
Cheminformatics. 9:242017. View Article : Google Scholar
|
|
31
|
Wang Z: Package ‘bst’: Gradient Boosting.
Version 0.3–15. https://cran.r-project.org/web/packages/bst/bst.pdfJuly
23–2018
|
|
32
|
Therneau T and Atkinson B: Package
‘rpart’: Recursive Partitioning and Regression Trees. Version
4.1–13. https://cran.r-project.org/web/packages/rpart/rpart.pdfFebruary
23–2018August 30–2018
|
|
33
|
Luo M, Wang XS, Roth BL, Golbraikh A and
Tropsha A: Application of quantitative structure-activity
relationship models of 5-HT1A receptor binding to
virtual screening identifies novel and potent 5-HT1A
ligands. J Chem Inf Model. 54:634–647. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
34
|
Pourbasheer E, Vahdani S, Malekzadeh D,
Aalizadeh R and Ebadi A: QSAR Study of 17β-HSD3 inhibitors by
genetic algorithm-support vector machine as a target receptor for
the treatment of prostate cancer. Iran J Pharm Res. 16:966–980.
2017.PubMed/NCBI
|
|
35
|
Meyer D, Dimitriadou E, Hornik K,
Weingessel A and Leisch F; e1071: Misc Functions of the Department
of Statistics, . Probability Theory Group (Formerly: E1071), TU
Wien. Version 1.6–8. https://rdrr.io/rforge/e1071/May 31–2017
|
|
36
|
Cai C, Fang J, Guo P, Wang Q, Hong H,
Moslehi J and Cheng F: In silico pharmacoepidemiologic evaluation
of drug-induced cardiovascular complications using combined
classifiers. J Chem Inf Model. 58:943–956. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
37
|
Roy K, Kar S and Ambure P: On a simple
approach for determining applicability domain of QSAR models.
Chemometr Intell Lab Syst. 145:22–29. 2015. View Article : Google Scholar
|
|
38
|
Li S: Package ‘rknn’: Random KNN
Classification and Regression. https://cran.r-project.org/web/packages/rknn/rknn.pdfJune
7–2015
|
|
39
|
Warnes GR, Bolker B and Lumley T: gtools:
various R programming tools. R Foundation for Statistical
Computing; Vienna: 2015
|
|
40
|
Sahigara F, Ballabio D, Todeschini R and
Consonni V: Defining a novel k-nearest neighbours approach to
assess the applicability domain of a QSAR model for reliable
predictions. J Cheminform. 5:272013. View Article : Google Scholar : PubMed/NCBI
|
|
41
|
Williams K: Package ‘ldbod’: Local
Density-Based Outlier Detection. Version 0.1.2. https://cran.r-project.org/web/packages/ldbod/ldbod.pdfMay
26–2017
|
|
42
|
Xu C, Cheng F, Chen L, Du Z, Li W, Liu G,
Lee PW and Tang Y: In silico prediction of chemical Ames
mutagenicity. J Chem Inf Model. 52:2840–2847. 2012. View Article : Google Scholar : PubMed/NCBI
|
|
43
|
Gower JC: A general coefficient of
similarity and some of its properties. Biometrics. 27:8571971.
View Article : Google Scholar
|
|
44
|
Miladiyah I, Jumina J, Haryana SM and
Mustofa M: Biological activity, quantitative structure-activity
relationship analysis, and molecular docking of xanthone
derivatives as anticancer drugs. Drug Des Devel Ther. 12:149–158.
2018. View Article : Google Scholar : PubMed/NCBI
|
|
45
|
Yadav DK, Kumar S, Saloni S, Singh H, Kim
MH, Sharma P, Misra S and Khan F: Molecular docking, QSAR and ADMET
studies of withanolide analogs against breast cancer. Drug Des
Devel Ther. 11:1859–1870. 2017. View Article : Google Scholar : PubMed/NCBI
|
|
46
|
Gaikwad R, Ghorai S, Amin SA, Adhikari N,
Patel T, Das K, Jha T and Gayen S: Monte Carlo based modelling
approach for designing and predicting cytotoxicity of
2-phenylindole derivatives against breast cancer cell line MCF7.
Toxicol In Vitro. 52:23–32. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
47
|
Abdelhaleem EF, Abdelhameid MK, Kassab AE
and Kandeel MM: Design and synthesis of thienopyrimidine urea
derivatives with potential cytotoxic and pro-apoptotic activity
against breast cancer cell line MCF-7. Eur J Med Chem.
143:1807–1825. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
48
|
Feher M and Ewing T: Global or local QSAR:
Is there a way out? QSAR Comb Sci. 28:850–855. 2009. View Article : Google Scholar
|
|
49
|
He Y, Zhu Q, Chen M, Huang Q, Wang W, Li
Q, Huang Y and Di W: The changing 50% inhibitory concentration
(IC50) of cisplatin: A pilot study on the artifacts of the MTT
assay and the precise measurement of density-dependent
chemoresistance in ovarian cancer. Oncotarget. 7:70803–70821. 2016.
View Article : Google Scholar : PubMed/NCBI
|
|
50
|
Sebaugh JL: Guidelines for accurate
EC50/IC50 estimation. Pharm Stat. 10:128–134. 2011. View Article : Google Scholar : PubMed/NCBI
|
|
51
|
Kryshchyshyn A, Devinyak O, Kaminskyy D,
Grellier P and Lesyk R: Development of predictive QSAR models of
4-thiazolidinones antitrypanosomal activity using modern machine
learning algorithms. Mol Inform. 37:e17000782018. View Article : Google Scholar : PubMed/NCBI
|
|
52
|
Cortes-Ciriano I, Bender A and Malliavin
TE: Comparing the influence of simulated experimental errors on 12
machine learning algorithms in bioactivity modeling using 12
diverse data sets. J Chem Inf Model. 55:1413–1425. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
53
|
Dearden JC: The use of topological indices
in QSAR and QSPR modeling. Advances in QSAR modeling. Roy K: 24.
Springer International Publishing; Cham: pp. 57–88. 2017,
View Article : Google Scholar
|
|
54
|
Karbakhsh R and Sabet R: Application of
different chemometric tools in QSAR study of azolo-adamantanes
against influenza A virus. Res Pharm Sci. 6:23–33. 2011.PubMed/NCBI
|
|
55
|
Prachayasittikul V, Pingaew R,
Anuwongcharoen N, Worachartcheewan A, Nantasenamat C,
Prachayasittikul S, Ruchirawat S and Prachayasittikul V: Discovery
of novel 1,2,3-triazole derivatives as anticancer agents using QSAR
and in silico structural modification. Springerplus. 4:5712015.
View Article : Google Scholar : PubMed/NCBI
|
|
56
|
Fereidoonnezhad M, Faghih Z, Mojaddami A,
Rezaei Z and Sakhteman A: A comparative QSAR analysis, molecular
docking and PLIF studies of some N-arylphenyl-2,
2-dichloroacetamide analogues as anticancer agents. Iran J Pharm
Res. 16:981–998. 2017.PubMed/NCBI
|
|
57
|
Edraki N, Das U, Hemateenejad B, Dimmock
JR and Miri R: Comparative QSAR analysis of 3,5-bis
(arylidene)-4-piperidone derivatives: The development of predictive
cytotoxicity models. Iran J Pharm Res. 15:425–437. 2016.PubMed/NCBI
|
|
58
|
Akbari S, Zebardast T, Zarghi A and
Hajimahdi Z: QSAR modeling of COX-2 inhibitory activity of some
dihydropyridine and hydroquinoline derivatives using multiple
linear regression (MLR) method. Iran J Pharm Res. 16:525–532.
2017.PubMed/NCBI
|
|
59
|
Fassihi A and Sabet R: QSAR study of
p56(lck) protein tyrosine kinase inhibitory activity of flavonoid
derivatives using MLR and GA-PLS. Int J Mol Sci. 9:1876–1892. 2008.
View Article : Google Scholar : PubMed/NCBI
|
|
60
|
Rojas C, Todeschini R, Ballabio D, Mauri
A, Consonni V, Tripaldi P and Grisoni F: A QSTR-based expert system
to predict sweetness of molecules. Front Chem. 5:532017. View Article : Google Scholar : PubMed/NCBI
|
|
61
|
Mohanapriya A and Achuthan D: Comparative
QSAR analysis of cyclo-oxygenase2 inhibiting drugs. Bioinformation.
8:353–358. 2012. View Article : Google Scholar : PubMed/NCBI
|
|
62
|
Chavan S, Nicholls IA, Karlsson BC,
Rosengren AM, Ballabio D, Consonni V and Todeschini R: Towards
global QSAR model building for acute toxicity: Munro database case
study. Int J Mol Sci. 15:18162–18174. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
63
|
Wittwer MB, Zur AA, Khuri N, Kido Y,
Kosaka A, Zhang X, Morrissey KM, Sali A, Huang Y and Giacomini KM:
Discovery of potent, selective multidrug and toxin extrusion
transporter 1 (MATE1, SLC47A1) inhibitors through prescription drug
profiling and computational modeling. J Med Chem. 56:781–795. 2013.
View Article : Google Scholar : PubMed/NCBI
|
|
64
|
Dai Y, Chen N, Wang Q, Zheng H, Zhang X,
Jia S, Dong L and Feng D: Docking analysis and multidimensional
hybrid QSAR model of 1,4-benzodiazepine-2,5-diones as HDM2
antagonists. Iran J Pharm Res. 11:807–830. 2012.PubMed/NCBI
|
|
65
|
Sharma BK, Singh P, Pilania P, Shekhawat M
and Prabhakar YS: QSAR of 2-(4-methylsulphonylphenyl) pyrimidine
derivatives as cyclooxygenase-2 inhibitors: Simple structural
fragments as potential modulators of activity. J Enzyme Inhib Med
Chem. 27:249–260. 2012. View Article : Google Scholar : PubMed/NCBI
|
|
66
|
Sharma BK, Verma S and Prabhakar YS:
Topological and physicochemical characteristics of
1,2,3,4-Tetra-hydroacridin-9(10H)-ones and their antimalarial
profiles: A composite insight to the structure-activity relation.
Curr Comput Aided Drug Des. 9:317–335. 2013. View Article : Google Scholar : PubMed/NCBI
|
|
67
|
Rasouli Y and Davood A: Hybrid Docking -
QSAR studies of 1,4-dihydropyridine-3, 5-dicarboxamides as
potential antitubercular agents. Curr Comput Aided Drug Des.
14:35–53. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
68
|
Li H, Panwar B, Omenn GS and Guan Y:
Accurate prediction of personalized olfactory perception from
large-scale chemoinformatic features. Gigascience. 7:72018.
View Article : Google Scholar
|
