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MultiDK: A Multiple Descriptor Multiple Kernel Approach for Molecular Discovery and Its Application to the Discovery of Organic Flow Battery Electrolytes Sung-Jin Kim, Adrián Jinich, and Alán Aspuru-Guzik J. Chem. Inf. Model., Just Accepted Manuscript • Publication Date (Web): 22 Mar 2017 Downloaded from http://pubs.acs.org on March 22, 2017

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Journal of Chemical Information and Modeling

MultiDK: A Multiple Descriptor Multiple Kernel Approach for Molecular Discovery and Its Application to the Discovery of Organic Flow Battery Electrolytes Sungjin Kim, Adrián Jinich, and Alán Aspuru-Guzik∗ Department of Chemistry and Chemical Biology, Harvard University, 12 Oxford Street, Cambridge, Massachusetts 02138 E-mail: [email protected]

Abstract We propose a multiple descriptor multiple kernel (MultiDK) method for efficient molecular discovery using machine learning. We show that the MultiDK method improves both the speed and accuracy of molecular property prediction. We apply the method to the discovery of electrolyte molecules for aqueous redox flow batteries. Using multiple-type - as opposed to single-type - descriptors, we obtain more relevant features for machine learning. Following the principle of ’wisdom of the crowds’, the combination of multiple-type descriptors significantly boosts prediction performance. Moreover, by employing multiple kernels - more than one kernel functions for a set of the input descriptors - MultiDK exploits nonlinear relations between molecular structure and properties better than a linear regression approach. The multiple kernels consist of a Tanimoto similarity kernel and a linear kernel for a set of binary descriptors and a set of non-binary descriptors, respectively. Using MultiDK, we achieve an average

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performance of r2 = 0.92 with a test set of molecules for solubility prediction. We also extend MultiDK to predict pH-dependent solubility, and apply it to a set of quinone molecules with different ionizable functional groups to assess their performance as flow battery electrolytes.

Introduction Aqueous organic flow batteries are emerging as a low-cost alternative to store renewable energy. 1–5 For example, Huskinson et al., Yang et al., and Liu et al. experimentally showed that high capacity energy storage can be achieved using earth abundant organic electrolytes such as quinone molecules. 6,7 Given the vast molecular space covered by all the possible quinone molecules, high-throughput computational screening 8–20 has emerged as an important strategy to find electrolytes that satisfy the stringent requirement of aqueous flow batteries. In particular, the flow battery system requires a redox potential greater than 0.9V for a catholyte and less than 0.2V for an anolyte, as well as a solubility greater than one molar for both electrolytes. 1 Moreover, quinone electrolytes operating in acidic (pH = 0) are demonstrated by Huskinson et al. 1 and Yang et al. 2 and alkaline (pH =14) environments were by Lin et al. 3 , highlighting the need to predict performance at different pH conditions. Recent high-throughput computational screening of benzo-, naphtho-, anthra-, and thiophenoquinone libraries 21,22 demonstrated that the reduction potential of these redox couples can be predicted accurately using quantum chemistry methods coupled to linear regression against experimental data sets. Using the free energy of solvation as a proxy descriptor, the molecular solubility of electrolytes was also predicted in both references. Here, we build upon this work by developing a machine learning strategy that results in stronger correlations with experimental solubility data. The computational prediction of molecular solubility has been a research topic for decades, with most research being driven by the field of drug discovery. 6,23,24 However, predicting the solubility of organic electrolytes is particularly challenging, given the stringent target solu2


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bilities and the extreme pH values of flow battery electrolyte solutions. 25 While the target solubility of drug molecules is generally less than 0.1 molar, the target for flow battery organic electrolytes can be more than 1 molar. Moreover, molecular libraries to screen potential flow battery electrolytes include extremely acidic 25 or basic organic molecules 3 while the majority of drug candidates are relatively weak acids and bases. 18,23,26,27 Both machine learning and quantum chemical approaches can be used to estimate molecular solubility. Whereas machine learning approaches predict solubility based on training to experimental data, 28–30 quantum chemistry aims to predict solubility from first principles. 21,31–33 Although quantum chemical approaches are preferable for obtaining a mechanistic understanding of underlying principles, 24,31 our focus here is on machine learning approaches which facilitate high-throughput molecular discovery with low computational cost. 28,34,35 Machine learning approaches can be categorized into three types of methods according to the types of descriptors used: property-based methods, structure-based methods, and functional group-based methods. Property-based methods predict physicochemical values based on molecular properties which can be measured experimentally or obtained from computational approaches. One such property used for solubility estimation is the partition coefficient, the logarithm of which is denoted as logP. 36–39 Several methods have been proposed to calculate logP. 40–43 The general solubility estimation method (GSE), with its extended and modified variants, is an example of a property-type method which estimates logS from logP. 36–39,44 On the other hand, structure-based methods rely on the estimation of solubility as a function of molecular structure. Structure is usually represented by a binary fingerprint, which captures molecular topology, connectivity, or fragment information. 45,46 Finally, group-based methods partition molecules into functional groups, and the contribution of each to the value of a physicochemical property is estimated by calibration with available experimental data. 47–49 Property-based methods generally involve fewer regression parameters than the other two

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approaches, but require additional computation in order to estimate the required intermediate properties included in the descriptor set. If large experimental data is available for intermediate properties such as logP, property-based methods can predict solubility for a wider range of molecules than any of the other methods. 50,51 However, a significant gap between logP-based estimation and experimental solubility remains. 38 Large efforts have been devoted to reduce this gap by adding more input information to the set of descriptors, with a concomitant increase in the complexity of the regressions employed. 23 Two examples of property-based methods, the GSE approach and Delaney’s extended GSE (EGSE) approach, rely on two and three fitted parameters, respectively. Delaney shows that the performance of GSE and EGSE were r2 (GSE) = 0.67 and r2 (EGSE) = 0.69 for a dataset of 1305 compounds compiled by the authors, 38 which highlights the gap between prediction and experiment for such methodologies. Structure-based methods predict solubility directly from molecular structural information, which can be implemented by various types of descriptors. 46,52–54 Generally, binary fingerprints offer a good trade-off between simplicity and predictive power. 45,49,55 We recently developed neural fingerprints which are structure-based and application-specific with input descriptors generated for arbitrary size and shape based on a molecular graph. 54 Zhou et al. predicted molecular solubility using a binary circular fingerprint descriptor. 45 Although they demonstrated a prediction accuracy of r2 = 0.83, the authors had to carefully select the training data set in order to achieve that value. Huuskonen showed that a prediction accuracy of r2 = 0.92 can be achieved by using non-binary descriptors consisting of 53 parameters, including 39 atom-type electro-topological state (E-state) indices. 25 However, non-binary descriptors significantly increase computational cost in both the training and validation stages, especially when feature selection is encountered during the regression process. 56,57 A different binary fingerprint approach has been investigated by Lind and Maltseva, in which support vector regression (SVR) employing the Tanimoto similarity kernel is applied in order to overcome the limit of the multiple linear regression method. 55 The binary

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approach developed by Lind and Maltseva achieved r2 = 0.88. The group-based methods integrate contributions of all associated functional groups mulP tiplied by the number of each functional group in a compound: C0 + N i=1 Ci Gi where Gi is the number of times the ith group appears in the compound, C0 is a constant bias parameter, and Ci is the contribution of the ith group. 47 Hou et al. proposed an atom contribution method, which overcomes the ’missing fragment’ problem in pure group contribution methods. 58 The atom contribution method categorizes atoms together with their surrounding molecular environment. Cheng et al. used functional key descriptors such as MACCS Keys and PC881 instead of directly counting the number of instances of each functional group. This approach simplifies descriptors by assigning them binary values but still requires large training data sets and can neglect certain molecular fragments. Moreover, Cheng et al. apply these descriptors for a solubility classification task with a much lower solubility requirement, 10 µg/mL, than the threshold values necessary for aqueous flow battery applications. The ability to carry out solubility predictions that account for pH-dependence is critical to discovering molecules for aqueous flow batteries. In addition to requiring very high solubility, the pH at which an organic flow battery system is designed to operate varies depending on the required redox potential values and other experimental considerations. For instance, negative electrolytes of 9,10-anthraquinone-2,7-disulphonic acid (AQDS) 1 and 2,6-dihydroxyanthraquinone (DHAQ) 3 require 1 molar solubility at pH 0 and pH 14, respectively. While prediction methods for intrinsic solubility have been widely discussed, methods to predict pH-dependent solubility have remained less explored. 24,26,27,59–61 In theory, the Henderson-Hasselbach relationship can be used to predict pH-dependent solubility based on the intrinsic solubility of a molecule. 60 However, the limitations of current pKa prediction accuracies as well as the salt plateau phenomena of ionic solubility motivates the use of a data-driven approaches. This requires significantly more experimental training data (i.e. solubility as a function of pH) than intrinsic solubility prediction. 27,61 Moreover, the intrinsic solubility of extremely strong acids with a negative pKa value has not been well

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investigated in the literature. In high-throughput molecular screening, the development of an accurate and cost-effective property estimation method is a key factor to successfully find new candidate molecules. 54,62,63 In this work, we develop a fast and accurate property estimation method for high-throughput molecular discovery. We name the proposed approach a multiple descriptor multiple kernel (MultiDK) method. The method relies on combining an ensemble of different descriptors, including fingerprints, functional keys, as well as other molecular physicochemical properties. We also apply different kernels for different types of descriptors in order to capture nonlinear relations between fingerprints and properties. 55 The MultiDK approach supports intrinsic solubility estimation and, when combined with external tools to predict pH-dependent partition coefficients, can be used to predict pH-dependent solubility.

Methods Datasets and tools We evaluated the performance of MultiDK on four different datasets for intrinsic solubility, i.e., equilibrium solubility at pH conditions where the molecules are entirely neutral. The entire datasets are provided as supporting information in the four different papers. The four datasets are 496 molecules used by Willighagen et al., 64 , 1140 molecules used by Delaney, 38 , 1676 molecules used by Wang et al., 65 , 3310 molecules in a collection of ci800406y_si_ 002.xls, ci800406y_si_003.xls, ci800406y_si_004.xls, ci800406y_si_005.xls data sheets used by Wang et al. 39 We generated molecular descriptors from canonical simplified molecular-input line-entry system (SMILES) strings which are obtained from either SYBYL line notation (SLN), international chemical identifier (InChI) or SMILES strings in the original datasets using the RDKit package. 66 Also, all duplicated molecules with the same experiment solubility values are dropped in each dataset to get cross-validation performance fairly. The cross-validation were performed using the K-fold approach with K = 20 6


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Specifically, it uses a nonlinear binary kernel for binary descriptors and linear kernel for nonbinary descriptors. To optimize kernel functions, 71–73 multiple combinatorial kernels have been used in various applications including biomedical data and YouTube video data. 74–76 . Here, we use a multiple kernel approach to apply appropriate kernels for different features instead of training the kernel. The binary kernel function of kB (·) exploits a nonlinear relationship between molecular structure and properties. The nonlinear relationships arise primarily because each bit indicates the presence or absence of a pattern rather than a quantitative value. MultiDK uses all training molecules as support vector molecules for kernel processing similar to support vector machines. We use the Tanimoto similarity kernel, which has been used in a wide range of machine learning applications, such as exploiting binary feature information to recognize white images on a black background 77 as well as a kernel for support vector and Gaussian progress regression in molecular property prediction. 8,55 In the MultiDK approach, ensemble learning is employed based on multiple combinational descriptors according to the principle of the ’wisdom of the crowds’. 78 The set of descriptors in MultiDK includes the Morgan circular fingerprints, 53 MACCS Keys 46 and three non-binary molecular properties. The three types of descriptors represent structural information (atom, path), key patterns (fragments, functional group) and associated molecular properties. We find that this ensemble combination is effective to predict molecular properties because both atom and subgroup representations are employed in the set of descriptors together with the related molecular properties. Moreover, we use different kernels for binary and non-binary descriptors. Particularly, a binary similarity kernel is applied to the binary descriptor and a linear kernel for the non-binary descriptor. We evaluate the methods with training and cross-validation phases. In the training phase, we optimize the regression parameters using Ridge regularization, which is equivalent to minimizing J=

L X

2

|yo (i) − y(i)| + α

i=1

M X

wj2

(3)

j=1

where yo (i) and y(i) are the ith experimental and predicted value, respectively, wj is the 9



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jth weight belonging to either binary and non-binary parts, M is the number of the total deployed descriptors, and α is the Ridge hyperparameter. 79 The descriptor consists of 4096 binary bits of the Morgan circular fingerprint with radius 6, 117 binary bits of the MACCS Keys and a few non-binary scalar descriptors. We generate all descriptors using the RDKit tool 66 except for the partition coefficient, which we obtain from Cxcalc in the Chemaxon Marvin suite. 80 Before linear regression, we pre-process the 4213 binary bits with the binary similarity kernel by calculating Tanimoto similarity between an input vector and the set of training vectors. We pass the non-binary descriptors directly to the linear regression stage without pre-processing. Then, the binary kernel output values and the direct nonbinary output value are entered into the Ridge linear regression stage. We employ the Ridge regression routine in the scikit-learn Python package. 81 The regularization process produces the optimal regression coefficients corresponding to the maximum R2 performance. In the cross-validation phase, a combination vector of the binary kernel outputs and a direct descriptor of a test molecule is multiplied by the coefficients obtained in the training phase.

MultiDK for estimating intrinsic solubility, logS We predict solubility using MultiDK as follows:

log S =

X

WSP · xWSP ) + w0 wiCK kB (xCK , xCK i ) + (w

(4)

i=1,...,L

where kB (·) is a binary kernel function, xCK is a concatenated binary descriptor of the Morgan circular fingerprint (xC ) and the MACCS keys (xK ) and xWSP is a concatenated non-binary descriptor of the molecular weight (xW ), Labute’s approximate surface area 82 (xS ), and logP (xP ). Both wiCK and wWSP are regression coefficients corresponding to xCK and xWSP , respectively, and w0 is a regression intercept. We generate all descriptors including xC , xK , xW , xS , and xP according to the SMILES string of each molecule.

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MultiDK for estimating pH dependent solubility, logS(pH) In order to predict pH-dependent solubility, we extend the MultiDK method as follows:

log S(pH) = log S + log P − log D(pH)

(5)

where log P and log D(pH) are the n-octanol-to-water partition coefficient and the pHdependent distribution coefficient, respectively. Since the two coefficients can be approximated as log P = log SOct − log S and log D(pH) = log SOct − log S(pH), 36,37 we are able to extend MultiDK as in (5) where log SOct is solubility in octanol. The octanol solubility is intrinsic and therefore determined regardless of existence of ionizable groups. 83 We evaluate both log P and log D(pH) using the cxcalc plugin in the Chemaxon Marvin suite. 84 Alternatively, ACD (http://acdlabs.com/) and Chemspider (http://chemspider.com) can also be used to calculate both log P and log D(pH) as an application package and an on-line website, respectively. If precisely estimated pKa values are available, the Henderson-Hasselbalch approach can yield pH-dependent solubility from the intrinsic solubility as well. 27,60,85

Results and Discussion Cross-validation results For cross-validation, we consider five approaches which are summarized with their associated descriptors in Table 1, where x and y of MDxy and MultiDKxy represents the number of embodied binary descriptors and the number of embodied non-binary descriptors, respectively. Performance of MultiDK for solubility prediction We use the distribution of r2 values in a 20-fold cross validation as a metric of prediction performance. The r2 distribution is obtained by repeating 20 times for both training and testing 11



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Table 1: Different approaches with their associated descriptors Method SD MD21 MD23 MultiDK10 MultiDK21 MultiDK23

xC O O O O O O

xK

xW

xS

xP

O O

O O

O

O

O O

O O

O

O

until 20 subsets of data are used for validation. Figure 3 shows the r2 distribution obtained with each of the methods tested as a function of the Ridge regression hyper-parameter α. For this estimation, we used the 1676 unique SMILES and solubility molecules use by Wang et al. 65 For efficient comparison, only one non-binary descriptor is considered in this evaluation. Both the MultiDK and the MD methods employ two binary and one non-binary descriptor: Morgan fingerprints (MFP), MACCS Keys (MACCS) and molecular weight (MolW). Figure 3 shows that MultiDK and MD significantly outperform SD. Moreover, MultiDK is most robust to changes in the value of α. This result reveals that additional group and property information help improve prediction accuracy. In Figure 4, the performances of SD, MD and MultiDK are compared when the optimal value of α is used, where the SD family includes MFP, MACCS and MolW. This bar graph shows a clear difference between the SD family, MD and MultiDK approaches. The best α value were found by grid search, selecting α on the basis of regression performance in the range of 10−3 to 102 with 10 logarithmically equally spaced steps. At each step, regression was evaluated using a 20-fold cross-validation with initial data shuffling. SD (MFP), MD and MultiDK achieve their best regression coefficient values of E[r2 ] ± std(r2 ) = 0.72 ± 0.04, 0.86 ± 0.04 and 0.89 ± 0.03 at α = 10.0, 31.6 and 0.03, respectively, where E[r2 ] is the mean of squared correlation coefficients and std(r2 ) is the standard deviation (std) of squared correlation coefficients. This result highlights three important points. First, SD with MFP outperforms the other two SDs approaches, SD using MACCS and SD using MolW. It 12


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best α. We obtained the following cross-validation summary statistics: mean(r2 ) = 0.91, std(r2 ) = 0.027, root mean squared error (RMSE) = 0.61, mean absolute error (MAE) = 0.45, median absolute error (MedAE) = 0.33. We also compare a cross-validation performance of MultiDK with an alternative multiple kernel method, namely multi-kernel fusion. 86–89 The multi-kernel fusion method generates a new kernel by, for example, averaging multiple basic kernels. 89 We consider three basic kernels including linear, Gaussian and Tanimoto similarity kernels and generated three fused kernels from them: the linear and Gaussian kernel average, the Gaussian and (modified) Tanimoto similarity kernel average, and the linear, Gaussian and (modified) Tanimoto similarity kernel average, denoted as Fused-LG, Fused-GT∗ , Fused-LGT∗ . It is noteworthy that in order to apply to the fused method, we modify the Tanimoto similarity kernel as follows:

kT′ M (x, y) = kT M (fb (x), fb (y))

(6)

where kT M (x, y) is the original Tanimoto similarity kernel, and fb (x) is a function returning only a binary part of an input descriptor. Otherwise, the Tanimoto similarity kernel cannot be used for kernel fusion with ensemble descriptors that combine binary and non-binary descriptors, e.g., descriptors used in MultiDK23. The r2 performance comparisons are shown in Figure 7, which reveals that MultiDK23 outperforms a general fused method (Fused-LG: r2 = 0.87±0.03) and modified fused methods (Fused-GT∗ : r2 = 0.90±0.02 and Fused-LGT∗ : r2 = 0.88 ± 0.02). Table 2: 20-fold cross-validation performances of the 1676 molecules Method SD MD21 MD23 MultiDK10 MultiDK21 MultiDK23

Best α 1E+1 3E+1 3E+1 1E-3 3E-2 1E-1

15

E[r2 ] 0.72 0.86 0.88 0.80 0.89 0.91

std(r2 ) 0.06 0.05 0.03 0.04 0.04 0.03




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Figure 8: Prediction performance of different methods on the dataset with 496 molecules.


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Table 3: Performances of solubility prediction for different datasets Method SD MD21 MD23 MultiDK10 MultiDK21 MultiDK23

496 Best α 1E+1 1E+1 1E+1 3E-3 7E-2 7E-2

molecules E[r2 ] std(r2 ) 0.65 0.12 0.84 0.07 0.88 0.06 0.70 0.11 0.86 0.06 0.89 0.05

1140 molecules Best α E[r2 ] std(r2 ) 1E+1 0.71 0.09 1E+1 0.87 0.04 1E+1 0.89 0.03 3E-3 0.79 0.05 3E-2 0.90 0.04 3E-2 0.92 0.02

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3310 molecules Best α E[r2 ] std(r2 ) 3E+1 0.66 0.05 3E+1 0.79 0.06 3E+1 0.83 0.02 3E-2 0.77 0.05 1E-1 0.85 0.03 1E-1 0.87 0.04



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molecule types or the attached R-groups, all three methods predict the intrinsic solubility (logS) of the molecules to be below zero log-molar. Thus, all molecules have intrinsic solubility, unionized solubility, lower than the target solubility of aqueous flow battery electrolytes. Table 4: Predicted intrinsic solubility of 27 quinone molecules by three different methods, i.e., MultiDK, VCCLAB and EGSE, where Benzoquinone (BQ), naphthoquinone (NQ) and anthraquinone (AQ), with available unique positions of R-group attachment.

pH-dependent solubility for single R-group quinones In Figure 14, 15 and 16, we show the pH-dependent solubility predicted by the extended MultiDK method for BQ, NQ and AQ family molecules, respectively, whereas the collection of them are illustrated as a heat map in Figure 17. We applied the extended method to the three 22


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solubility, and applied it to various quinones with strong acidic or alkaline functional groups at different pH values where the quinones are the candidates of electrolytes for organic aqueous flow batteries.

Associated Content Supporting Information: The supporting information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jcim.xxx. This document includes three supporting items: Comparing MultiDK with other machine learning methods such as support vector machine (SVM) and deep neural networks (DNN), analyzing the binary kernel function used in MultiDK, and comparing logS(pH) estimations of MultiDK and Cxcalc for seven experimental data.

Acknowledgement This work was funded by the U.S. DOE ARPA-E award DE-AR0000348. The computing time was provided by Harvard FAS Research Computing. We thank Roy G. Gordon and Michael J. Aziz for helpful discussions. The support of Changwon Suh and Rafael GómezBombarel was useful in this work.

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