# Predicting Highly Enantioselective Catalysts Using Tunable Fragment Descriptors**

Nobuya Tsuji, Pavel Sidorov, Chendan Zhu, Yuuya Nagata, Timur Gimadiev, Alexandre Varnek, Benjamin List

► To cite this version:

Nobuya Tsuji, Pavel Sidorov, Chendan Zhu, Yuuya Nagata, Timur Gimadiev, et al.. Predicting Highly Enantioselective Catalysts Using Tunable Fragment Descriptors**. Angewandte Chemie International Edition, 2023, 62 (11), pp.e202218659. 10.1002/anie.202218659. hal-05110148

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Machine Learning

How to cite: Angew. Chem. Int. Ed. 2023, 62, e202218659

International Edition: doi.org/10.1002/anie.202218659

German Edition: doi.org/10.1002/ange.202218659

# Predicting Highly Enantioselective Catalysts Using Tunable Fragment Descriptors**

Nobuya Tsuji<sup>†</sup>,* Pavel Sidorov<sup>†</sup>,* Chendan Zhu, Yuuya Nagata, Timur Gimadiev, Alexandre Varnek,* and Benjamin List*

**Abstract:** Catalyst optimization processes typically rely on inductive and qualitative assumptions of chemists based on screening data. While machine learning models using molecular properties or calculated 3D structures enable quantitative data evaluation, costly quantum chemical calculations are often required. In contrast, readily available binary fingerprint descriptors are time- and cost-efficient, but their predictive performance remains insufficient. Here, we describe a machine learning model based on fragment descriptors, which are fine-tuned for asymmetric catalysis and represent cyclic or polyaromatic hydrocarbons, enabling robust and efficient virtual screening. Using training data with only moderate selectivities, we designed theoretically and validated experimentally new catalysts showing higher selectivities in a challenging asymmetric tetrahydropyran synthesis.

In asymmetric catalysis, screening processes toward satisfactory selectivity require considerable effort and time, and they typically rely on trial-and-error experimentation. Chemists often identify optimal catalysts with good enantio-

selectivities using “educated guessing”, in other words, by considering steric, electronic, and conformational effects of the catalysts. However, the efficiency of this process largely depends on the scientists involved, and even when such “predictions” work, the outcome is typically more qualitative in nature. Developing tools that enable a quantitative evaluation of screening data is highly desirable.[1–3]

Machine learning is recognized as a practical tool to quantify molecular properties in this context, such as biological activity or chemical synthesis. Historically, quantitative explanations and predictions of reactivity and selectivity from structural information follow Hammett’s approach, where steric and electronic properties of substituents at specific positions in a series of substrates or catalysts act as equation parameters. The variables are obtained by either experiment[4,5] or quantum chemical computation,[6,7] and they are widely applied to the optimization of catalytic processes, including asymmetric ones using multivariate linear regression models or non-linear models.[8–13] While this approach can provide insights into the reaction mechanism, as the calculation resources are limited, the choice of those descriptors typically relies on the knowledge of chemists, thus rendering the model difficult to consider general features of the catalyst structure.[10]

Alternatively, 3D descriptors represent general structural information, and thus models on 3D structures may identify correlations without requiring knowledge of the detailed reaction mechanism.[14] These methods have already been applied in asymmetric catalysis, using calculated 3D structures with grid-based binary feature vectors, such as Comparative Molecular Field Analysis (CoMFA).[15–19] The Denmark group reported a modification of grid-based methods with non-binary features based on the distribution of conformations to account for their flexibility and demonstrated that the model could predict higher selectivity even in the absence of such examples in the training data set.[20] The disadvantage of such methods is the requirement of costly quantum chemical calculations and, in the case of grid-based methods, the necessity of alignment of core structures. Although several 3D structure-based methods that do not require alignment have also been described in the literature, their performances, especially in extrapolation, are limited.[3,21–23]

In contrast, 2D descriptors, such as fragment counts or binary fingerprints, also represent general structural features, albeit often implicitly, and allow for avoiding costly calculations, as they are derived directly from a 2D representation of a molecule.[24–27] Using 2D descriptors,

[*] Dr. N. Tsuji,† Dr. P. Sidorov,† Dr. Y. Nagata, Dr. T. Gimadiev, Prof. Dr. A. Varnek, Prof. Dr. B. List
Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University
Sapporo 001-0021 (Japan)
E-mail: tsuji@icredd.hokudai.ac.jp
pavel.sidorov@icredd.hokudai.ac.jp

Dr. C. Zhu, Prof. Dr. B. List
Max-Planck-Institut für Kohlenforschung
45470 Mülheim an der Ruhr (Germany)
E-mail: list@mpi-muelheim.mpg.de

Prof. Dr. A. Varnek
Laboratory of Chemoinformatics, UMR 7140, CNRS,
University of Strasbourg
67081 Strasbourg (France)
E-mail: varnek@unistra.fr

[†] These authors contributed equally to this work.

[**] A previous version of this manuscript has been deposited on a preprint server (https://doi.org/10.26434/chemrxiv-2022-bsmdl).

<figure><img src="image_3.png" /></figure>

© 2023 The Authors. Angewandte Chemie International Edition published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
therefore, has a clear advantage in speed. Nonetheless,
fingerprint descriptors by themselves are considered insuffi-
cient in representing the structures to construct reliable
predictive models in asymmetric catalysis.[28–35] Recently,
multiple fingerprint features were employed together to
build predictive models. Despite the relatively good per-
formance of the test set on substrates, the validation sets for
catalysts still had room for improvement,[36] which confirms
the inherent difficulty of representing complicated catalyst
structures solely relying on the binary 2D fingerprints.
Indeed, simply employing conventional fingerprints still
leads to significant challenges if simple but robust machine
learning models are used; thus, a relatively large number of
training data with multiple optimization cycles were re-
quired, as described by Belyk, Sherer, and co-workers.[35]
Alternatively, fragment count descriptors, represented by
ISIDA (In Silico Design and Data Analysis) platform,[37]
encode the fragments into the non-binary vectors based on
the number of occurrences in a molecule without limiting
the number of features. ISIDA descriptors, in particular,
offer a wide variety of possible representations of the
chemical structures, including topology (linear, atom-cen-
tered fragments, atom pairs, or triplets) and size of frag-
ments, which allows for fine-tuning them to the problem at
hand. Furthermore, the ISIDA platform enables to calculate
fragments for reaction schemes as a whole via Condensed
Graphs of Reaction (CGR),[38] a special representation that
combines reactants and products into a single pseudo-
molecule containing dynamic (i.e., changing during the
reaction) bonds, which is a unique property of this approach.
Although these descriptors have been utilized in the
modeling of pharmacological properties,[39,40] materials,[41]

and chemical transformations,[42] their application to asym-
metric catalysis has never been described.

Here, we report a predictive model for the enantioselectivity of structurally diverse and flexible catalysts using fragment count descriptors based on the ISIDA platform, not requiring any quantum chemical calculations. For a more precise representation of cyclic or polyaromatic hydrocarbon substituents which are common in asymmetric catalysis, another fragment type is introduced. Further, its application to an actual synthetic challenge is demonstrated; highly selective catalysts for asymmetric 2,2-disubstituted tetrahydropyran synthesis have been predicted from the training data consisting of catalysts showing only moderate selectivities.

We initiated a preliminary study on the stereoselective
hydroalkoxylation reaction of simple alkenes to construct
2,2-disubstituted tetrahydrofuran rings using imidodiphos-
phorimidate (IDPi) catalysts under several different con-
ditions (35 examples, e.r. from 15:85 to 91:9; referenced
further as THF set, Figure S11 and S12).[43] These IDPi
catalysts possess a common scaffold yet have a variety of
substituents on the 3,3'-positions of BINOLs and nitrogen
substituents, represented as Ar and R (Figure 1A, only R =
Tf in THF set for the sake of simplicity).[44] The workflow is
summarized in Figure 1B. The structural information of
catalysts, substrates, and products is encoded by either
fingerprints or fragment descriptors. In the case of frag-
ments, the reaction schemes are transformed into the CGRs.
Structures of catalysts can be represented either as full
structures or only by the modulable substituents depicted as
Ar and R; the latter would reduce noise from the common
scaffold and enable the application of different descriptors

<figure><img src="image_4.png" /><figcaption>**Figure 1.** (A) The data set (THF) used in the initial study. (B) The general modeling workflow. Each catalyst/reaction combination has been encoded by different types of molecular descriptors forming several descriptors pools. Each descriptors pool was used to build a related Support Vector Regression model, the performance of which (determination coefficient, R²) was optimized in 10-fold cross-validation (CV). The best-performing model (model with maximum R²) is selected among those for external predictions.</figcaption></figure>
suitable for each substituent. Physico-chemical parameters of solvents are also included as descriptors in the model. Other reaction conditions, namely temperatures and concentrations, are separately considered; all data points are calibrated to an identical condition before constructing a predictive model, and the predicted free energy differences are calibrated back to each experimental condition, providing the predicted enantiomeric ratio in the given conditions (see Supporting Information for details). Thus, this workflow represents a generalized approach to handling catalysts, reaction schemes, and reaction conditions simultaneously.

With these strategies, preliminary benchmark studies were performed to compare the performance of various fingerprints and ISIDA fragment counts with which both atom-centered and linear fragments are considered. Due to the modest size of the data set, the Support Vector Machine (SVM) method was chosen as a widely used non-linear machine learning algorithm.[45] Descriptor space and model hyperparameters were optimized by genetic algorithms[46] in 10-fold cross-validation (CV). In our preliminary studies, ISIDA fragments have demonstrated better predictivity than conventional fingerprints (Figure 2A). However, there is still a concern about their intrinsic linearity. For instance, atom-centered fragments are represented as a collection of linear fragments sharing the same central atom, and thus they do not explicitly account for cyclic structures. While this is often considered to make the descriptors more general and insensitive to subtle changes in structures,[47] it may introduce ambiguity in the case of structures such as IDPi catalysts, which contain a large number of condensed aromatic rings, or particular substituents, like polycyclic hydrocarbons.

To account for this complexity, augmented substructure type descriptors called CircuS (Circular Substructures) were developed in the framework of the CGRtools library.[48]

They function similarly to ISIDA atom-centered fragments yet explicitly consider encountered full substructures with closed rings, representing complex structural motifs more specifically (Figure 2B). As expected, the CircuS performed even better than ISIDA descriptors; the fully optimized model built on CircuS fragments of catalyst substituents outperformed others with the satisfactory $R^2=0.905$, mean absolute error $MAE=0.741 \text{ kJ mol}^{-1}$ in leave-one-out (LOO) cross-validation (Figure 2A and 2C).

We then envisioned applying the established protocol for the computer-aided design of more selective catalysts for tetrahydropyran (THP) synthesis (Figure 3A). Taking advantage of the modularity of IDPi catalysts,[49–55] a variety of catalysts were prepared, providing a diversity on the 3,3'-positions of BINOL backbones represented as Ar and the sulfonyl groups on nitrogen atoms represented as R. For more efficient screening and data consistency, a synthesis robot was employed, streamlining the process from experiments to data generation (Figure S1). To build a predictive model for the THP synthesis, we then constructed common data set comprising 35 examples of THF and 35 examples of THP (Full data available in Figure S13) to ensure the diversity of the catalyst structures, which was evaluated by Tanimoto similarity coefficients (Figure S7).

The fully optimized model built on CircuS fragment descriptors provided a reasonable predictivity with $R^2_{CV}=0.878$, $MAE_{CV}=0.754 \text{ kJ mol}^{-1}$ (Figure 3B). Based on this, a virtual screening has been conducted. As the training data does not cover all the combinations of Ar and R groups of IDPi catalysts, additional 190 catalysts representing experimentally unseen combinations of those were prepared in silico, and their selectivities were virtually evaluated. As expected, among all the predictions, some catalysts were predicted to provide higher enantioselectivities than those experimentally obtained in the training set. Following these

<figure><img src="image_5.png" /><figcaption>**Figure 2.** (A) Descriptor benchmarking results (as mean absolute error, MAE, kJ mol⁻¹) on THF set in leave-one-out (LOO) cross-validation. The SVM model was optimized by genetic algorithms, and the best model's results are shown for several descriptor types (see Figure S9 for full data). (B) Fragments generated by CircuS at a given distance from center atoms. (C) LOO cross-validation results for the best model (CircuS fragments, solvent descriptors, catalysts represented by the Ar substituent) on the THF data set. Statistical parameters (MAE, R²) are indicated on the plot.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Figure 3.** (A) Target reaction (THP synthesis) to predict more selective catalyst. (B) 10-fold cross-validation results for the best model (Circus fragments, solvent descriptors, catalysts represented by the Ar and R substituent, and the reaction is represented as CGR) on the THF data set (black dot) and THP data set (black triangle). Dash line represents perfect prediction. Statistical parameters (MAE, R²) are indicated on the plot. Validation sets for THP are indicated in the red triangle. (C) Selected examples for comparison between experimental and predicted values under different reaction conditions. ΔΔG is represented in kJ·mol⁻¹. (D) Substrate scope with the optimal catalyst **61**. Isolated yields are presented. Enantiomeric ratio (e.r.) is determined either via high-performance liquid chromatography (HPLC) or gas chromatography (GC). [a] 0.025 M at 40 °C. Yields and selectivities in the brackets are the ones with the previously optimized catalyst and conditions for comparison (see Figure S2 for detail, yields were determined by ¹H NMR using CH₂Br₂ as an internal standard). [⁴³]</figcaption></figure>

results, additional catalysts spanning a wide range of predicted selectivity values were prepared and evaluated as a validation set. Also, three more catalysts with substituents that do not exist in the training sets were prepared and tested for the predictability of out-of-sample examples (See Figure S16 for more detailed control studies). Overall, predictions worked well (Figure 3B), and the most selective catalyst **61** indeed provided an enantiomeric ratio of 91.5:8.5, which is significantly higher than any examples from the training set (up to 82:18 e.r.). Additionally, by changing the temperature and concentration, an even higher enantiomeric ratio (96:4) was predicted, and this predicted value was almost identical to the experimentally observed value (Figure 3C).

Having been able to predict the optimal catalysts and conditions, we were keen on exploring other substrates. Phenyl-substituted substrate **1b** furnished an excellent isolated yield and enantioselectivity even on a larger scale. Naphthalene derivative **2c** was also obtained with excellent yield and enantioselectivity. The more challenging aliphatic substrates, both with primary and secondary alkyl groups, underwent the desired cyclization to give products **2d** and **2e** in excellent yields and enantioselectivities. The results are superior to the previously reported ones regarding both reactivity and selectivity, which underlines the efficacy of the presented method[⁴³] (Figure 3D and Figure S2).

To confirm the validity of the obtained predictive model, the performance of models with one-hot encoded substituents and random features were tested (Table S1). Poor results of these models indicate that the optimized model learns chemistry rather than just considering the combinations. Moreover, fragment descriptors allow the quantitative interpretation of the model—ColorAtom[⁵⁶] approach enables visualization of each atom's contribution to the selectivity (Figure S17 and Table S5).

To test the scope of the developed descriptors, our protocol was further applied to the data set of chiral phosphoric acid (CPA)-catalyzed asymmetric addition of thiols to imines reported by the Denmark group[²⁰] (Figure 4). The performance of the predictive model, especially on an external set of catalysts, is closer to the reported 3D descriptors than any other 2D descriptors, which indicates that the use of the described method is not limited to a specific reaction (Figure S18).

The methodology delineated here enables quantitative evaluation of catalyst selectivities, including the predictions of extrapolated examples. This approach is solely based on 2D structures and reaction conditions and does not require costly quantum mechanical calculation. While its applicability to a larger scope of catalysts and other, more complicated reactions must be further investigated, we believe it would
<figure><img src="image_7.png" /><figcaption>**Figure 4.** 10-fold cross-validation (CV) and external prediction (cat-test, sub-test, cat-sub-test) results of CPA-catalyzed asymmetric addition of thiols to imines represented by CircuS fragments. Composition of external test sets is explained in the original paper.[20]</figcaption></figure>

provide fast and robust predictions enabling optimization of various catalytic reactions beyond the training data.

## Code and data availability

Data used in the study (structures and enantioselectivities of catalysts, experimental conditions, calibration curves, prediction results) is available in the Supporting Information. Python libraries developed in the study are open to public and are available on GitHub: https://github.com/POSidorov/ChemInfoTools (CircuS fragment descriptors), https://github.com/POSidorov/HPLCAnalysis (analysis of experimental HPLC data). Full protocol of data processing and modeling is available as Jupyter Notebook in Supporting materials.

## Acknowledgements

We thank the generous support from the Max Planck Society, the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation, Leibniz Award to B. L.) and under Germany's Excellence Strategy (EXC 2033-390677874-RESOLV), the European Research Council (ERC, European Union's Horizon 2020 research and innovation program “C–H Acids for Organic Synthesis, CHAOS” Advanced Grant Agreement No. 694228). This work was also financially supported by the Institute for Chemical Reaction Design and Discovery (ICReDD), which

was established by the World Premier International Research Initiative (WPI), MEXT, Japan, and by JSPS KAKENHI Grants 21H01925, 22K14672, 21H01924 and JST-ERATO JPMJER1903. The authors thank Dr. Thomas Buyck and Dr. Jennifer L. Kennemur for screening data and preliminary studies. We also thank Dr. Roberta Properzi, Dr. Ravindra Raut, Dr. Yowei Xie, and Stefanie Dehn for analytical data of catalysts, and all group members for sharing IDPi catalysts. We also thank the technicians and the members of our NMR, MS, and chromatography groups for their excellent service. We also thank Instrumental Analysis Division, Global Facility Center, Creative Research Institution, Hokkaido University for HRMS with Thermo Fisher Scientific Exactive Plus. Open Access funding enabled and organized by Projekt DEAL.

## Conflict of Interest

The authors declare the following competing financial interest(s): A patent, WO2017037141 (A1), has been filed by the MPI fur Kohlenforschung covering the IDPi catalyst class and their applications in asymmetric synthesis.

## Data Availability Statement

The data that support the findings of this study are available in the supplementary material of this article.

**Keywords:** Asymmetric Catalysis · Machine Learning · Organocatalysis

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Manuscript received: December 17, 2022

Accepted manuscript online: January 23, 2023

Version of record online: February 6, 2023