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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.

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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.

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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).

|No. of compounds |Source of Data |dependent Variable |R2training |Q2CV |References |

|56 |1 literature |pIC50 |0.98-0.99 |0.94 |Pasha, et al. 2008 |

|48 |5 literatures |pIC50 |0.95-0.98 |0.83-0.86 |Xue, et al. 2008 |

|57 |Self-laboratory |pIC50 |0.98-0.99 |0.93 |Xue, et al. 2007 |

|32 |2 literatures |pIC50 |0.96 |0.96 |Park and Sung 2011 |

|22 |2 literatures |pIC50 |0.65-0.93 |NA |Tang and Chen 2015 |

|40 |9 literatures |pKi |0.85 |0.77-0.80 |Bazl, et al. 2013 |

|33 |Self- laboratory |pIC50 |0.91 |0.69 |Lee, et al. 2012 |

TABLE S2 The structures, the inhibitory activities and the relative IC50 (pRIC) values, as well as their original experiment reports for all inhibitors used in this work.

|Skeleton of inhibitors |substituent group(s) |

|Element Counts |4 descriptors including the counts of C, H, O and N atoms. |

|ALogP |2 descriptors including AlogP and AlogP_MR. |

|Molecular Properties |4 descriptors including Apol, Molecular Mass, solubility and Weight. |

|Molecular Property Counts |53 descriptors. |

|Surface Area and Volume |7 descriptors. |

|Topological Descriptors |43 descriptors. |

TABLE S4 The 32 employed features used to refine QSAR models.

| |Types of Features |Symbol of Features |Description |References |

|F01 |Structure-based features |ΔEvdW |Van der Waals terms decomposed from free energy. |Cui, et al. 2016 |

|F02 | |ΔEele |Electrostatic terms decomposed from free energy. | |

|F03 | |ΔGpolar |nonpolar solvation terms decomposed from free energy. | |

|F04 | |ΔGnonpolar |polar solvation terms decomposed from free energy. | |

|F05 |Element Counts |C_Count |The counts of oxygen atom. |Shahin, et al. 2016 |

|F06 | |O_Count |The counts of carbon atom. | |

|F07 |Molecular Property Counts|HBD_Count |The counts of hydrogen bond donors. | |

|F08 | |HBA_Count |The counts of hydrogen bond acceptors. | |

|F09 | |Num_H_Acceptors_Lipinski |The counts of hydrogen bond acceptors defined by Lipinski et. al.. | |

|F10 | |Num_Rings |The counts of rings. | |

|F11 | |Num_Rings6 |The counts of hexatomic rings. | |

|F12 | |Num_AromaticRings |The counts of aromatic rings. | |

|F13 | |Num_RingAssemblies |The counts of ring types. | |

|F14 | |Num_Chains |The counts of chains. | |

|F15 | |Num_ChainAssemblies |The counts of chain types. | |

|F16 | |Num_StereoBonds |The counts of stereo bonds leading cis- or trans- configurationrd4r. | |

|F17 | |Num_RotatableBonds |single bonds between heavy atoms that are both not in a ring and not terminal | |

|F18 |ALogP |ALogP |The predicted oil-water partition coefficients. | |

|F19 |Molecular Properties |Molecular_Solubility |Solubility of ligand. | |

|F20 |Surface Area and Volume |Molecular_FractionalPolarSASA |The ratio of the polar solvent accessible surface area divided by the total solvent accessible | |

| | | |surface area. | |

|F21 | |Molecular_PolarSurfaceArea |polar surface area. | |

|F22 |Topological Descriptors |Kappa_1_AM |Alpha-modified shape index of order one defined by Kier et. al.. | |

|F23 | |Kappa_3_AM |Alpha-modified shape index of order three defined by Kier et. al.. | |

|F24 | |IC |Information Content defined by Bonchev et. al.. | |

|F25 | |BIC |Bonding Information Content defined by Bonchev et. al.. | |

|F26 | |CIC |Complementary Information Content defined by Bonchev et. al.. | |

|F27 | |IAC_Mean |Mean Information of Atomic Composition defined by Bonchev et. al.. | |

|F28 | |CHI_V_3_P |valence-modified connectivity index defined by Kier et. al.. | |

|F29 | |CHI_V_3_C |valence-modified connectivity index defined by Kier et. al.. | |

|F30 | |SC_3_P |The number of third-order subgraphs in the molecular graph. | |

|F31 | |Wiener |Wiener index. | |

|F32 | |JX |Balaban 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.

| |pIC50 |pRIC |F01 |

| |Inhibitor |Kojic acid | | |

|Arbutin |6 500.00 |500.00 |13.00 |Kolbe, et al. 2013 |

| |547.00 |43.00 |12.72 |Curto, et al. 1999 |

| |55.00 |70.00 |0.79 |Jeong and Shim 2004 |

| |17.60 |7.70 |2.29 |Boissy, et al. 2005 |

| |1 1130.00 |10.00 |1 113.00 |Jones, et al. 2002 |

|Hydroquinone |4 400.00 |500.00 |8.80 |Kolbe, et al. 2013 |

| |653.00 |43.70 |14.94 |Curto, et al. 1999 |

| |33.48 |38.24 |0.88 |Song, et al. 2007 |

| |13.48/1 090.56 |128.64/18.57 |0.10/58.70a |Chiari, et al. 2011 |

| |10.02 |65.08 |0.15 |Cho, et al. 2012 |

| |37.00 |75.00 |0.49 |Criton and Mellay-Hamon 2008 |

|Resveratrol |26.80 |21.07 |1.27 |Ha, et al. 2012 |

| |13.48 |8.20 |1.64 |Chung, et al. 2012 |

a: the RIC value of hydroquinone determined by Maria et al. with L-tyrosine or L-DOPA as substrate was 0.10 or 58.70, respectively.

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( Fengchao Cui.

E-mail address: fccui@ciac.

(Yunqi Li.

E-mail address: yunqi@ciac.

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