ࡱ> W'' '''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''((W))i** /.bjbjބބ v4E`%E'U'e'e'e'$'''P')'\9/6666!8r<W>ZZZZZZZ, ^``Ze';?!8!8;?;?ZAe'e'66H[AAA;?e'6e'6 P A;?ZAAVG@vHLAG O[0\Gx#aA#avHAvH\e'H$;?;?;?ZZA;?;?;?\;?;?;?;?#a;?;?;?;?;?;?;?;?;?+ &:   Supporting Information Predictive models for tyrosinase inhibitors: challenges from heterogeneous activity data determined by different experimental protocols Haifeng Tang1,2,3, Fengchao Cui1(, Lunyang Liu1,3 ,Yunqi Li1( 1. Key Laboratory of Synthetic Rubber, Changchun Institute of Applied Chemistry (CIAC), Chinese Academy of Sciences, Changchun 130022, P. R. China. 2. School of Life Science, Jilin University, Changchun 130012, P. R. China. 3. University of Chinese Academy of Sciences, Beijing 100049, China.  FIGURE S1 (A) The plot of experiment IC50 and RIC values of hydroquinone (blue ) and inhibitors in DS_Dro (red triangle). The tyrosinase inhibitory activities, with systematic errors, of hydroquinone from diverse literatures distributed along principle line determined by inhibitors in DS_Dro. (B) The plot of R2OOB and Q2CV of the QSAR models against the numbers of hydroquinone combined to DS_Dro. The R2OOB and Q2CV increase gradually following sequentially removing the hydroquinone molecules with large systematic error one by one.  FIGURE S2 The importance ranking of 32 descriptors according mean decrease in predictive Mean Squared Errors (MSE) of QSAR model trained by RF algorithm. TABLE S1 Representative QSAR models for tyrosinase inhibitors constructed with regression algorithm. Logarithmic scale of half maximal inhibitory concentration (pIC50) and inhibition constant (pKi) have been used as dependent variables in training models. The qualities of QSAR models are evaluated by the correlation coefficient in training (R2training) and cross validation (Q2CV). 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Macromol.Int J Biol Macromol589-595622013Nov0141-8130WOS:000329881200086<Go to ISI>://WOS:00032988120008610.1016/j.ijbiomac.2013.09.056Zhu, et al. 2013 EMBED ChemDraw.Document.6.0 I4319.500 37.300 0.282 Yes HYPERLINK \l "_ENREF_32" \o "Wang, 2014 #10"  ADDIN EN.CITE Wang201410Wang, et al. 2014101017Wang, YanCurtis, Long, Marcus J.Lee, Byong WonYuk, Heung JooKim, Dae WookTan, Xue FeiPark, Ki HunInhibition of tyrosinase activity by polyphenol compounds from Flemingia philippinensis rootsBioorganic & Medicinal ChemistryBioorganic & Medicinal ChemistryBiorg. Med. 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Types of descriptorsThe counts of descriptorsElement Counts4 descriptors including the counts of C, H, O and N atoms.ALogP2 descriptors including AlogP and AlogP_MR.Molecular Properties4 descriptors including Apol, Molecular Mass, solubility and Weight.Molecular Property Counts53 descriptors.Surface Area and Volume7 descriptors.Topological Descriptors43 descriptors. TABLE S4 The 32 employed features used to refine QSAR models. Types of FeaturesSymbol of FeaturesDescriptionReferencesF01Structure-based featuresEvdWVan der Waals terms decomposed from free energy. HYPERLINK \l "_ENREF_8" \o "Cui, 2016 #22"  ADDIN EN.CITE Cui201622Cui, et al. 2016222217Cui, Feng ChaoLiu, Lun YangTang, Hai FengYang, Ke ChengLi, Yun QiConstruction of explicit models to correlate the structure and the inhibitory activity of aldose reductase: Flavonoids and sulfonyl-pyridazinones as inhibitorsChemical biology & drug designChemical Biology & Drug DesignChem. Biol. Drug Des.Chem Biol Drug Des482-491894
482
20162016-Sep-16MEDLINE:27637378<Go to ISI>://MEDLINE:27637378http://onlinelibrary.wiley.com/store/10.1111/cbdd.12868/asset/cbdd12868.pdf?v=1&t=j4kxx2w4&s=064d5632c2671a5cf06f294219186f334135ecea10.1111/cbdd.12868</Cite></EndNote>Cui, et al. 2016F02EeleElectrostatic terms decomposed from free energy.F03Gpolarnonpolar solvation terms decomposed from free energy.F04Gnonpolarpolar solvation terms decomposed from free energy.F05Element CountsC_CountThe counts of oxygen atom. HYPERLINK \l "_ENREF_29" \o "Shahin, 2016 #62"  ADDIN EN.CITE Shahin201662Shahin, et al. 2016626217Shahin, RandSwellmeen, LubnaShaheen, OmarAboalhaija, NourHabash, MahaIdentification of novel inhibitors for Pim-1 kinase using pharmacophore modeling based on a novel method for selecting pharmacophore generation subsetsJournal of Computer-Aided Molecular DesignJournal of Computer-Aided Molecular DesignJ. Comput. Aided Mol. Des.J Comput Aided Mol Des39-683012016Jan0920-654XWOS:000367595200004<Go to ISI>://WOS:00036759520000410.1007/s10822-015-9887-7Shahin, et al. 2016F06O_CountThe counts of carbon atom.F07Molecular Property CountsHBD_CountThe counts of hydrogen bond donors.F08HBA_CountThe counts of hydrogen bond acceptors.F09Num_H_Acceptors_LipinskiThe counts of hydrogen bond acceptors defined by Lipinski et. al..F10Num_RingsThe counts of rings.F11Num_Rings6The counts of hexatomic rings.F12Num_AromaticRingsThe counts of aromatic rings.F13Num_RingAssembliesThe counts of ring types.F14Num_ChainsThe counts of chains.F15Num_ChainAssembliesThe counts of chain types.F16Num_StereoBondsThe counts of stereo bonds leading cis- or trans- configurationrd4r.F17Num_RotatableBondssingle bonds between heavy atoms that are both not in a ring and not terminalF18ALogPALogPThe predicted oil-water partition coefficients.F19Molecular PropertiesMolecular_SolubilitySolubility of ligand.F20Surface Area and VolumeMolecular_FractionalPolarSASAThe ratio of the polar solvent accessible surface area divided by the total solvent accessible surface area.F21Molecular_PolarSurfaceAreapolar surface area.F22Topological DescriptorsKappa_1_AMAlpha-modified shape index of order one defined by Kier et. al..F23Kappa_3_AMAlpha-modified shape index of order three defined by Kier et. al..F24ICInformation Content defined by Bonchev et. al..F25BICBonding Information Content defined by Bonchev et. al..F26CICComplementary Information Content defined by Bonchev et. al..F27IAC_MeanMean Information of Atomic Composition defined by Bonchev et. al..F28CHI_V_3_Pvalence-modified connectivity index defined by Kier et. al..F29CHI_V_3_Cvalence-modified connectivity index defined by Kier et. al..F30SC_3_PThe number of third-order subgraphs in the molecular graph.F31WienerWiener index.F32JXBalaban indices. TABLE S5 The correlation matrix of pairwise descriptors used in training QSAR models. The Pearson and Spearman correlation coefficients (R2) are placed in upper and lower triangular matrix, respectively. The absolute values of R2 under 0.01 were represent with 0. The positive and negative correlation coefficients were indicated with black and red, respectively. pIC50pRICF01F02F03F04F05F06F07F08F09F10F11F12F13F14F15F16F17F18F19F20F21F22F23F24F25F26F27F28F29F30F31F32pIC5010.740.1100.020.070.0400.150.2500.070.110.040.0100.020.010.050.060.080.0600.040.010.030000.090.050.060.090.01pRIC0.7810.140.010.030.120.070.060.010.270.120.090.220.040.030.020.050.030.070.030.0600.070.100.030.110.02000.080.050.110.130.02F010.110.14100.150.840.510.030.140.360.090.480.300.550.440.230.530.160.350.480.600.250.010.460.360.280.020.180.030.470.150.470.580.35F020.010010.800.020.010.090.1400.0200.020.020.010.020.020.0800.07000.030.020.0100.120.130.09000.0100.02F030.020.100.230.5110.180.110.020.030.020.020.100.100.200.120.050.160.020.050.290.070.050.010.090.070.010.200.270.050.080.010.100.080.12F040.030.080.6600.2810.720.020.120.520.210.680.390.660.660.410.760.240.500.580.760.290.040.700.550.420.020.270.060.690.240.690.810.55F050.020.040.410.010.090.7100.130.570.200.820.710.540.480.780.680.330.480.680.810.410.030.920.610.610.010.2900.910.630.930.890.62F0600.020.010.150.050010.4800.340.020.090.080.070.070.020.0400.070.120.340.440.030.010.040.0300.190.010.040.010.010F070.150.030.090.320.060.040.080.4110.210.140.180.020.140.110.020.090.110.100.370.360.700.430.050.040.020.010.010.030.210.100.090.190.08F080.270.290.230.020.040.310.380.020.2310.350.630.380.350.480.410.490.210.460.270.280.250.080.650.400.510.010.080.120.680.410.680.740.41F090.020.100.100.030.140.230.240.160.040.4010.260.250.140.230.260.270.100.1200.020.070.810.400.230.410.040.030.110.200.110.350.270.18F100.050.050.300.010.070.540.790.010.120.470.2010.590.680.680.460.820.320.090.360.630.430.010.690.330.490.030.260.050.850.410.890.840.70F110.170.260.3000.080.420.690.080.030.490.190.6310.280.160.590.370.160.210.330.390.160.050.630.290.4600.190.070.570.560.710.520.43F120.010.020.4100.150.420.420.030.080.210.090.510.2810.690.140.790.050.300.500.500.3300.420.360.140.220.480.090.470.070.500.650.66F13000.2600.190.520.490.060.030.160.240.580.160.6010.170.920.200.100.210.420.260.050.460.370.230.090.320.290.550.070.520.660.67F1400.010.1900.050.490.790.020.020.370.250.540.550.160.2510.30.270.270.320.520.210.080.830.520.690.030.090.040.710.840.730.580.31F1500.010.3300.160.650.740.010.030.250.250.780.440.550.840.4810.220.110.290.560.290.050.610.450.330.100.400.110.670.150.690.770.81F160.020.020.100.0600.220.380.060.110.120.060.310.160.020.130.310.26100.140.260.140.010.300.110.430.1100.040.390.280.360.340.04F170.040.080.2200.080.250.2700.080.270.170.260.180.380.340.360.370.0310.450.270.160.070.590.860.230.020.260.030.420.200.360.600.40F180.070.030.360.060.030.340.490.050.240.330.010.590.390.640.440.350.560.150.4010.770.610.020.520.470.180.170.4400.610.310.530.640.48F19000.350.080.010.500.740.110.400.560.050.810.420.730.620.410.730.320.500.7510.630.010.660.520.360.050.290.040.800.360.720.880.59F200.0600.190.1900.220.360.350.690.190.020.390.140.250.230.210.250.190.100.590.7310.310.220.140.100.060.1400.480.280.300.370.29F2100.060.010.120.120.030.010.230.320.040.6500.020.010.020.030.020.010.040.060.080.3310.160.140.160.050.010.070.020.010.080.050.02F220.030.070.4000.160.650.810.070.010.520.490.610.640.370.380.780.580.260.370.370.460.140.1410.740.6700.2100.700.680.900.950.42F230.030.040.2300.060.340.4700.080.310.140.230.370.300.150.490.240.090.800.570.430.170.040.5810.330.020.330.010.340.290.470.530.40F240.030.080.220.010.030.400.630.010.030.490.400.510.500.100.180.720.380.440.20.170.320.130.090.700.3110.24000.620.590.710.560.21F250.010.010.010.020.080.0200.0200.020.01000.290.110.040.030.090.040.090.030.01000.020.2610.750.0100.0500.010.18F2600 0.1600.120.220.2300.030.050.050.160.170.500.260.090.2500.310.370.280.080.010.220.3300.67100.190.030.190.250.52F270000000.010.1500.020.190.010.070.040.130.040.020.0100.050.0200.2000.010.010.01010.030.050.010.060.03F280.060.030.300.040.060.560.860.010.150.450.240.810.540.340.500.700.650.380.180.430.650.4800.850.510.5700.15010.750.920.890.59F290.040.050.190.020.020.420.740.020.040.400.240.580.580.110.190.850.420.420.110.270.450.250.020.620.270.720.080.030.030.6310.630.450.18F300.050.080.3800.120.650.90.010.040.510.420.870.700.430.490.750.730.350.180.340.560.250.060.840.330.7000.1400.850.7610.900.51F310.020.050.3600.160.680.880.030.020.460.450.730.660.450.490.790.710.290.320.380.530.180.100.880.660.6300.2500.780.70.8710.55F32000.210.010.070.470.630.040.150.270.100.640.400.540.640.430.760.090.290.450.670.390.010.530.440.250.080.3600.550.290.580.621 TABLE S6 The IC50 and RIC values of some inhibitors reported by different literatures. InhibitorsIC50 (M)RICReferencesInhibitorKojic acidArbutin6 500.00500.0013.00 HYPERLINK \l "_ENREF_19" \o "Kolbe, 2013 #46"  ADDIN EN.CITE <EndNote><Cite><Author>Kolbe</Author><Year>2013</Year><RecNum>46</RecNum><DisplayText>Kolbe, et al. 2013</DisplayText>464617Kolbe, L.Mann, T.Gerwat, W.Batzer, J.Ahlheit, S.Scherner, C.Wenck, H.Staeb, F.4-n-butylresorcinol, a highly effective tyrosinase inhibitor for the topical treatment of hyperpigmentationJournal of the European Academy of Dermatology and VenereologyJournal of the European Academy of Dermatology and VenereologyJ. Eur. Acad. Dermatol. Venereol.J Eur Acad Dermatol VenereolJournal of the European Academy of Dermatology & Venereology19-2327Suppl. 12013Jan0926-9959WOS:000312146400005<Go to ISI>://WOS:00031214640000510.1111/jdv.12051
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