1
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Xu J, Ye X, Lv Z, Chen YH, Wang XS. The Role of Base in Reaction Performance of Photochemical Synthesis of Thiazoles: An Integrated Theoretical and Experimental Study. Chemistry 2024; 30:e202304279. [PMID: 38409580 DOI: 10.1002/chem.202304279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 02/25/2024] [Accepted: 02/26/2024] [Indexed: 02/28/2024]
Abstract
Artificial intelligence (AI)/machine learning (ML) is emerging as pivotal in synthetic chemistry, offering revolutionary potential in retrosynthetic analysis, reaction conditions and reaction prediction. We have combined chemical descriptors, primarily based on Density Functional Theory (DFT) calculations, with various AI/ML tools such as Multi-Layer Perceptron (MLP) and Random Forest (RF), to predict the synthesis of 2-arylbenzothiazole in photoredox reactions. Significantly, our models underscore the critical role of the molecular structure and physicochemical characteristics of the base, especially the total atomic polarizabilities, in the rate-determining steps involving cyclohexyl and phenethyl moieties of the substrate. Moreover, we validated our findings in articles through experimental studies. It showcases the power of AI/ML and quantum chemistry in shaping the future of organic chemistry.
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Affiliation(s)
- Jiaxin Xu
- The Institute for Advanced Studies (IAS), Wuhan University, Wuhan, 430072, China
| | - Xiaoyu Ye
- The Institute for Advanced Studies (IAS), Wuhan University, Wuhan, 430072, China
| | - Zongchao Lv
- The Institute for Advanced Studies (IAS), Wuhan University, Wuhan, 430072, China
- CMC Pharmaceutical Research Center, Wuhan RS Pharmaceutical Co., Ltd., Wuhan, 430073, China
| | - Yi-Hung Chen
- The Institute for Advanced Studies (IAS), Wuhan University, Wuhan, 430072, China
| | - Xiang Simon Wang
- Howard University College of Pharmacy, 2300 Fourth Street NW, Washington, DC 20059, United States
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2
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Han J, Kwon Y, Choi YS, Kang S. Improving chemical reaction yield prediction using pre-trained graph neural networks. J Cheminform 2024; 16:25. [PMID: 38429787 PMCID: PMC10905905 DOI: 10.1186/s13321-024-00818-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Accepted: 02/19/2024] [Indexed: 03/03/2024] Open
Abstract
Graph neural networks (GNNs) have proven to be effective in the prediction of chemical reaction yields. However, their performance tends to deteriorate when they are trained using an insufficient training dataset in terms of quantity or diversity. A promising solution to alleviate this issue is to pre-train a GNN on a large-scale molecular database. In this study, we investigate the effectiveness of GNN pre-training in chemical reaction yield prediction. We present a novel GNN pre-training method for performance improvement.Given a molecular database consisting of a large number of molecules, we calculate molecular descriptors for each molecule and reduce the dimensionality of these descriptors by applying principal component analysis. We define a pre-text task by assigning a vector of principal component scores as the pseudo-label to each molecule in the database. A GNN is then pre-trained to perform the pre-text task of predicting the pseudo-label for the input molecule. For chemical reaction yield prediction, a prediction model is initialized using the pre-trained GNN and then fine-tuned with the training dataset containing chemical reactions and their yields. We demonstrate the effectiveness of the proposed method through experimental evaluation on benchmark datasets.
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Affiliation(s)
- Jongmin Han
- Department of Industrial Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon, Republic of Korea
| | - Youngchun Kwon
- Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon, Republic of Korea
| | - Youn-Suk Choi
- Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon, Republic of Korea.
| | - Seokho Kang
- Department of Industrial Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon, Republic of Korea.
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3
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Shirasawa R, Takaki K, Miyao T. Generalizability Improvement of Interpretable Symbolic Regression Models for Quantitative Structure-Activity Relationships. ACS OMEGA 2024; 9:9463-9474. [PMID: 38434845 PMCID: PMC10905595 DOI: 10.1021/acsomega.3c09047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 01/20/2024] [Accepted: 01/26/2024] [Indexed: 03/05/2024]
Abstract
In the pursuit of optimal quantitative structure-activity relationship (QSAR) models, two key factors are paramount: the robustness of predictive ability and the interpretability of the model. Symbolic regression (SR) searches for the mathematical expressions that explain a training data set. Thus, the models provided by SR are globally interpretable. We previously proposed an SR method that can generate interpretable expressions by humans. This study introduces an enhanced symbolic regression method, termed filter-induced genetic programming 2 (FIGP2), as an extension of our previously proposed SR method. FIGP2 is designed to improve the generalizability of SR models and to be applicable to data sets in which cost-intensive descriptors are employed. The FIGP2 method incorporates two major improvements: a modified domain filter to eradicate diverging expressions based on optimal calculation and the introduction of a stability metric to penalize expressions that would lead to overfitting. Our retrospective comparative analysis using 12 structure-activity relationship data sets revealed that FIGP2 surpassed the previously proposed SR method and conventional modeling methods, such as support vector regression and multivariate linear regression in terms of predictive performance. Generated mathematical expressions by FIGP2 were relatively simple and not divergent in the domain of function. Taken together, FIGP2 can be used for making interpretable regression models with predictive ability.
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Affiliation(s)
- Raku Shirasawa
- Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan
- Advanced Research Laboratory, Technology Infrastructure Center, Technology Platform, Sony Group Corporation, Atsugi Tec., 4-14-1 Asahi-cho, Atsugi-shi, Kanagawa 243-0014, Japan
| | - Katsushi Takaki
- Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan
| | - Tomoyuki Miyao
- Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan
- Data Science Center, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan
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4
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Shi R, Yu G, Huo X, Yang Y. Prediction of chemical reaction yields with large-scale multi-view pre-training. J Cheminform 2024; 16:22. [PMID: 38403627 PMCID: PMC10895839 DOI: 10.1186/s13321-024-00815-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 02/14/2024] [Indexed: 02/27/2024] Open
Abstract
Developing machine learning models with high generalization capability for predicting chemical reaction yields is of significant interest and importance. The efficacy of such models depends heavily on the representation of chemical reactions, which has commonly been learned from SMILES or graphs of molecules using deep neural networks. However, the progression of chemical reactions is inherently determined by the molecular 3D geometric properties, which have been recently highlighted as crucial features in accurately predicting molecular properties and chemical reactions. Additionally, large-scale pre-training has been shown to be essential in enhancing the generalization capability of complex deep learning models. Based on these considerations, we propose the Reaction Multi-View Pre-training (ReaMVP) framework, which leverages self-supervised learning techniques and a two-stage pre-training strategy to predict chemical reaction yields. By incorporating multi-view learning with 3D geometric information, ReaMVP achieves state-of-the-art performance on two benchmark datasets. Notably, the experimental results indicate that ReaMVP has a significant advantage in predicting out-of-sample data, suggesting an enhanced generalization ability to predict new reactions. Scientific Contribution: This study presents the ReaMVP framework, which improves the generalization capability of machine learning models for predicting chemical reaction yields. By integrating sequential and geometric views and leveraging self-supervised learning techniques with a two-stage pre-training strategy, ReaMVP achieves state-of-the-art performance on benchmark datasets. The framework demonstrates superior predictive ability for out-of-sample data and enhances the prediction of new reactions.
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Affiliation(s)
- Runhan Shi
- Department of Computer Science and Engineering, and Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Gufeng Yu
- Department of Computer Science and Engineering, and Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Xiaohong Huo
- Shanghai Key Laboratory for Molecular Engineering of Chiral Drugs, Frontiers Science Center for Transformative Molecules, School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yang Yang
- Department of Computer Science and Engineering, and Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
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5
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Okada H, Maeda S. On Accelerating Substrate Optimization Using Computational Gibbs Energy Barriers: A Numerical Consideration Utilizing a Computational Data Set. ACS OMEGA 2024; 9:7123-7131. [PMID: 38371820 PMCID: PMC10870292 DOI: 10.1021/acsomega.3c09066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 01/05/2024] [Accepted: 01/16/2024] [Indexed: 02/20/2024]
Abstract
Substrate optimization is a time- and resource-consuming step in organic synthesis. Recent advances in chemo- and materials-informatics provide systematic and efficient procedures utilizing tools such as Bayesian optimization (BO). This study explores the possibility of reducing the required experiments further by utilizing computational Gibbs energy barriers. To thoroughly validate the impact of using computational Gibbs energy barriers in BO-assisted substrate optimization, this study employs a computational Gibbs energy barrier data set in the literature and performs an extensive numerical investigation virtually regarding the Gibbs energy barriers as virtual experimental results and those with systematic and random noises as virtual computational results. The present numerical investigation shows that even the computational reactivity affected by noises of as much as 20 kJ/mol helps reduce the number of required experiments.
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Affiliation(s)
- Hiroaki Okada
- Graduate
School of Chemical Sciences and Engineering, Hokkaido University, Sapporo, Hokkaido 060-8628, Japan
| | - Satoshi Maeda
- Department
of Chemistry, Graduate School of Science, Hokkaido University, Sapporo, Hokkaido 060-0810, Japan
- Institute
for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Sapporo, Hokkaido 001-0021, Japan
- ERATO
Maeda Artificial Intelligence for Chemical Reaction Design and Discovery
Project, Hokkaido University, Sapporo, Hokkaido 060-0810, Japan
- Research
and Services Division of Materials Data and Integrated System (MaDIS), National Institute for Materials Science (NIMS), Tsukuba, Ibaraki 305-0044, Japan
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6
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Lei Q, Li L, Chen H, Wang X. Emerging Directions for Carbon Capture Technologies: A Synergy of High-Throughput Theoretical Calculations and Machine Learning. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:17189-17200. [PMID: 37917731 DOI: 10.1021/acs.est.3c05305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/04/2023]
Abstract
As the world grapples with the challenges of energy transition and industrial decarbonization, the development of carbon capture technologies presents a promising solution. The Scalable Modeling, Artificial Intelligence (AI), and Rapid Theoretical calculations, referred as SMART here, is an interdisciplinary approach that combines high-throughput calculation and data-driven modeling with expertise from chemical, materials, environmental, computer and data science and engineering, leading to the development of advanced capabilities in simulating and optimizing carbon capture processes. This perspective discusses the state-of-the-art material discovery research enabled by high-throughput calculation and data-driven modeling. Further, we propose a framework for material discovery, and illustrate the synergies among deep learning models, pretrained models, and comprehensive data sets, emerging as a robust framework for data-driven design and development in carbon capture. In essence, the adoption of the SMART approach promises a revolutionary impact on efforts in energy transition and industrial decarbonization.
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Affiliation(s)
- Qi Lei
- Department of Chemical Engineering, Tsinghua University, Beijing 100084, P. R. China
| | - Lanyu Li
- Department of Chemical Engineering, Tsinghua University, Beijing 100084, P. R. China
| | - Honghao Chen
- Department of Chemical Engineering, Tsinghua University, Beijing 100084, P. R. China
| | - Xiaonan Wang
- Department of Chemical Engineering, Tsinghua University, Beijing 100084, P. R. China
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7
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Gandolfi M, Ceotto M. Molecular Dynamics of Artificially Pair-Decoupled Systems: An Accurate Tool for Investigating the Importance of Intramolecular Couplings. J Chem Theory Comput 2023; 19:6093-6108. [PMID: 37698951 PMCID: PMC10536992 DOI: 10.1021/acs.jctc.3c00553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Indexed: 09/14/2023]
Abstract
We propose a numerical technique to accurately simulate the vibrations of organic molecules in the gas phase, when pairs of atoms (or, in general, groups of degrees of freedom) are artificially decoupled, so that their motion is instantaneously decorrelated. The numerical technique we have developed is a symplectic integration algorithm that never requires computation of the force but requires estimates of the Hessian matrix. The theory we present to support our technique postulates a pair-decoupling Hamiltonian function, which parametrically depends on a decoupling coefficient α ∈ [0, 1]. The closer α is to 0, the more decoupled the selected atoms. We test the correctness of our numerical method on small molecular systems, and we apply it to study the vibrational spectroscopic features of salicylic acid at the Density Functional Theory ab initio level on a fitted potential. Our pair-decoupled simulations of salicylic acid show that decoupling hydrogen-bonded atoms do not significantly influence the frequencies of stretching modes, but enhance enormously the out-of-plane wagging and twisting motions of the hydroxyl and carboxyl groups to the point that the carboxyl and hydroxyl groups may overcome high potential energy barriers and change the salicylic acid conformation after a short simulation time. In addition, we found that the acidity of salicylic acid is more influenced by the dynamical couplings of the proton of the carboxylic group with the carbon ring than with the hydroxyl group.
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Affiliation(s)
- Michele Gandolfi
- Dipartimento di Chimica, Università degli Studi di Milano, via Golgi 19, 20133 Milano, Italy
| | - Michele Ceotto
- Dipartimento di Chimica, Università degli Studi di Milano, via Golgi 19, 20133 Milano, Italy
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8
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Ohno M, Hayashi Y, Zhang Q, Kaneko Y, Yoshida R. SMiPoly: Generation of a Synthesizable Polymer Virtual Library Using Rule-Based Polymerization Reactions. J Chem Inf Model 2023; 63:5539-5548. [PMID: 37604495 PMCID: PMC10498440 DOI: 10.1021/acs.jcim.3c00329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Indexed: 08/23/2023]
Abstract
Recent advances in machine learning have led to the rapid adoption of various computational methods for de novo molecular design in polymer research, including high-throughput virtual screening and inverse molecular design. In such workflows, molecular generators play an essential role in creation or sequential modification of candidate polymer structures. Machine learning-assisted molecular design has made great technical progress over the past few years. However, the difficulty of identifying synthetic routes to such designed polymers remains unresolved. To address this technical limitation, we present Small Molecules into Polymers (SMiPoly), a Python library for virtual polymer generation that implements 22 chemical rules for commonly applied polymerization reactions. For given small organic molecules to form a candidate monomer set, the SMiPoly generator conducts possible polymerization reactions to generate an exhaustive list of potentially synthesizable polymers. In this study, using 1083 readily available monomers, we generated 169,347 unique polymers forming seven different molecular types: polyolefin, polyester, polyether, polyamide, polyimide, polyurethane, and polyoxazolidone. By comparing the distribution of the virtually created polymers with approximately 16,000 real polymers synthesized so far, it was found that the coverage and novelty of the SMiPoly-generated polymers can reach 48 and 53%, respectively. Incorporating the SMiPoly library into a molecular design workflow will accelerate the process of de novo polymer synthesis by shortening the step to select synthesizable candidate polymers.
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Affiliation(s)
- Mitsuru Ohno
- Daicel
Corporation, Kita-ku, 530-0011 Osaka, Japan
| | - Yoshihiro Hayashi
- The
Institute of Statistical Mathematics, Research Organization of Information
and Systems, Tachikawa, Tokyo 190-8562, Japan
- The
Graduate University for Advanced Studies, SOKENDAI, Tachikawa, Tokyo 190-8562, Japan
| | - Qi Zhang
- The
Institute of Statistical Mathematics, Research Organization of Information
and Systems, Tachikawa, Tokyo 190-8562, Japan
| | - Yu Kaneko
- Daicel
Corporation, Kita-ku, 530-0011 Osaka, Japan
| | - Ryo Yoshida
- The
Institute of Statistical Mathematics, Research Organization of Information
and Systems, Tachikawa, Tokyo 190-8562, Japan
- The
Graduate University for Advanced Studies, SOKENDAI, Tachikawa, Tokyo 190-8562, Japan
- National
Institute for Materials Science, 305-0047 Ibaraki, Japan
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9
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Chen K, Chen G, Li J, Huang Y, Wang E, Hou T, Heng PA. MetaRF: attention-based random forest for reaction yield prediction with a few trails. J Cheminform 2023; 15:43. [PMID: 37038222 PMCID: PMC10084704 DOI: 10.1186/s13321-023-00715-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 03/21/2023] [Indexed: 04/12/2023] Open
Abstract
Artificial intelligence has deeply revolutionized the field of medicinal chemistry with many impressive applications, but the success of these applications requires a massive amount of training samples with high-quality annotations, which seriously limits the wide usage of data-driven methods. In this paper, we focus on the reaction yield prediction problem, which assists chemists in selecting high-yield reactions in a new chemical space only with a few experimental trials. To attack this challenge, we first put forth MetaRF, an attention-based random forest model specially designed for the few-shot yield prediction, where the attention weight of a random forest is automatically optimized by the meta-learning framework and can be quickly adapted to predict the performance of new reagents while given a few additional samples. To improve the few-shot learning performance, we further introduce a dimension-reduction based sampling method to determine valuable samples to be experimentally tested and then learned. Our methodology is evaluated on three different datasets and acquires satisfactory performance on few-shot prediction. In high-throughput experimentation (HTE) datasets, the average yield of our methodology's top 10 high-yield reactions is relatively close to the results of ideal yield selection.
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Affiliation(s)
- Kexin Chen
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, New Territories, Hong Kong SAR
| | | | | | - Yuansheng Huang
- College of Pharmaceutical Sciences, Zhejiang University, Zhejiang, China
| | - Ercheng Wang
- Zhejiang Lab, Zhejiang, China
- College of Pharmaceutical Sciences, Zhejiang University, Zhejiang, China
| | - Tingjun Hou
- College of Pharmaceutical Sciences, Zhejiang University, Zhejiang, China
| | - Pheng-Ann Heng
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, New Territories, Hong Kong SAR
- Zhejiang Lab, Zhejiang, China
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10
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Motojima K, Shiratsuchi R, Suzuki K, Aizawa M, Kaneko H. Machine Learning Model for Predicting the Material Properties and Bone Formation Rate and Direct Inverse Analysis of the Model for New Synthesis Conditions of Bioceramics. Ind Eng Chem Res 2023. [DOI: 10.1021/acs.iecr.3c00332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
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11
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Noto N, Yada A, Yanai T, Saito S. Machine-Learning Classification for the Prediction of Catalytic Activity of Organic Photosensitizers in the Nickel(II)-Salt-Induced Synthesis of Phenols. Angew Chem Int Ed Engl 2023; 62:e202219107. [PMID: 36645619 DOI: 10.1002/anie.202219107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2022] [Revised: 01/15/2023] [Accepted: 01/16/2023] [Indexed: 01/17/2023]
Abstract
Catalytic systems using a small amount of organic photosensitizer for the activation of an inorganic (on-demand ligand-free) nickel(II) salt represent a cost-effective method for cross-coupling reactions, while C(sp2 )-O bond formation remains less developed. Herein, we report a strategy for the synthesis of phenols with a nickel(II) salt and an organic photosensitizer, which was identified via an investigation into the catalytic activity of 60 organic photosensitizers consisting of various electron donor and acceptor moieties. To examine the effect of multiple intractable parameters on the catalytic activity of photosensitizers, machine-learning (ML) models were developed, wherein we embedded descriptors representing their physical and structural properties, which were obtained from DFT calculations and RDKit, respectively. The study clarified that integrating both DFT- and RDKit-derived descriptors in ML models balances higher "precision" and "recall" across a wide range of search space relative to using only one of the two descriptor sets.
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Affiliation(s)
- Naoki Noto
- Integrated Research Consortium on Chemical Sciences (IRCCS), Nagoya University, Nagoya, Aichi, 464-8602, Japan
| | - Akira Yada
- Interdisciplinary Research Center for Catalytic Chemistry, National Institute of Advanced Industrial Science and Technology (AIST), 1-1-1 Higashi, Tsukuba, Ibaraki, 305-8565, Japan
| | - Takeshi Yanai
- Institute of Transformative Bio-Molecules (WPI-ITbM) and Graduate School of Science, Nagoya University, Nagoya, Aichi, 464-8602, Japan
| | - Susumu Saito
- Integrated Research Consortium on Chemical Sciences (IRCCS) and Graduate School of Science, Nagoya University, Nagoya, Aichi, 464-8602, Japan
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12
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Dotson JJ, van Dijk L, Timmerman JC, Grosslight S, Walroth RC, Gosselin F, Püntener K, Mack KA, Sigman MS. Data-Driven Multi-Objective Optimization Tactics for Catalytic Asymmetric Reactions Using Bisphosphine Ligands. J Am Chem Soc 2023; 145:110-121. [PMID: 36574729 DOI: 10.1021/jacs.2c08513] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Optimization of the catalyst structure to simultaneously improve multiple reaction objectives (e.g., yield, enantioselectivity, and regioselectivity) remains a formidable challenge. Herein, we describe a machine learning workflow for the multi-objective optimization of catalytic reactions that employ chiral bisphosphine ligands. This was demonstrated through the optimization of two sequential reactions required in the asymmetric synthesis of an active pharmaceutical ingredient. To accomplish this, a density functional theory-derived database of >550 bisphosphine ligands was constructed, and a designer chemical space mapping technique was established. The protocol used classification methods to identify active catalysts, followed by linear regression to model reaction selectivity. This led to the prediction and validation of significantly improved ligands for all reaction outputs, suggesting a general strategy that can be readily implemented for reaction optimizations where performance is controlled by bisphosphine ligands.
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Affiliation(s)
- Jordan J Dotson
- Department of Chemistry, University of Utah, Salt Lake City, Utah 84112, United States
| | - Lucy van Dijk
- Department of Chemistry, University of Utah, Salt Lake City, Utah 84112, United States
| | - Jacob C Timmerman
- Department of Small Molecule Process Chemistry, Genentech, Inc., South San Francisco, California 94080, United States
| | - Samantha Grosslight
- Department of Chemistry, University of Utah, Salt Lake City, Utah 84112, United States
| | - Richard C Walroth
- Department of Small Molecule Process Chemistry, Genentech, Inc., South San Francisco, California 94080, United States
| | - Francis Gosselin
- Department of Small Molecule Process Chemistry, Genentech, Inc., South San Francisco, California 94080, United States
| | - Kurt Püntener
- Synthetic Molecules Technical Development, Process Chemistry & Catalysis, F. Hoffmann-La Roche Limited, CH-4070 Basel, Switzerland
| | - Kyle A Mack
- Department of Small Molecule Process Chemistry, Genentech, Inc., South San Francisco, California 94080, United States
| | - Matthew S Sigman
- Department of Chemistry, University of Utah, Salt Lake City, Utah 84112, United States
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13
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Yarish D, Garkot S, Grygorenko OO, Radchenko DS, Moroz YS, Gurbych O. Advancing molecular graphs with descriptors for the prediction of chemical reaction yields. J Comput Chem 2022; 44:76-92. [PMID: 36264601 DOI: 10.1002/jcc.27016] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 08/31/2022] [Accepted: 09/05/2022] [Indexed: 11/08/2022]
Abstract
Chemical yield is the percentage of the reactants converted to the desired products. Chemists use predictive algorithms to select high-yielding reactions and score synthesis routes, saving time and reagents. This study suggests a novel graph neural network architecture for chemical yield prediction. The network combines structural information about participants of the transformation as well as molecular and reaction-level descriptors. It works with incomplete chemical reactions and generates reactants-product atom mapping. We show that the network benefits from advanced information by comparing it with several machine learning models and molecular representations. Models included logistic regression, support vector machine, CatBoost, and Bidirectional Encoder Representations from Transformers. Molecular representations included extended-connectivity fingerprints, Morgan fingerprints, SMILESVec embeddings, and textual. Classification and regression objectives were assessed for each model and feature set. The goal of each classification model was to separate zero- and non-zero-yielding reactions. The models were trained and evaluated on a proprietary dataset of 10 reaction types. Also, the models were benchmarked on two public single reaction type datasets. The study was supplemented with analysis of data, results, and errors, as well as the impact of steric factors, side reactions, isolation, and purification efficiency. The supplementary code is available at https://github.com/SoftServeInc/yield-paper.
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Affiliation(s)
| | - Sofiya Garkot
- SoftServe, Inc., Lviv, Ukraine.,Ukrainian Catholic University, Lviv, Ukraine
| | - Oleksandr O Grygorenko
- Enamine Ltd., Kyiv, Ukraine.,Taras Shevchenko National University of Kyiv, Kyiv, Ukraine
| | - Dmytro S Radchenko
- Enamine Ltd., Kyiv, Ukraine.,Taras Shevchenko National University of Kyiv, Kyiv, Ukraine
| | - Yurii S Moroz
- Taras Shevchenko National University of Kyiv, Kyiv, Ukraine.,Chemspace LLC, Kyiv, Ukraine
| | - Oleksandr Gurbych
- Lviv Polytechnic National University, Lviv, Ukraine.,Blackthorn AI, Ltd., London, UK
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14
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Asahara R, Miyao T. Extended Connectivity Fingerprints as a Chemical Reaction Representation for Enantioselective Organophosphorus-Catalyzed Asymmetric Reaction Prediction. ACS OMEGA 2022; 7:26952-26964. [PMID: 35936487 PMCID: PMC9352214 DOI: 10.1021/acsomega.2c03812] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Accepted: 07/07/2022] [Indexed: 06/15/2023]
Abstract
Predicting the outcomes of organic reactions using data-driven approaches aids in the acceleration of research. In laboratory-scale experiments, only a small number of reaction data can be accessed for machine learning model construction, where reaction representations play a pivotal role in the success of model construction. Nevertheless, representation comparison for a small data set is not adequate. Herein, focusing on the enantioselectivity of phosphoric-acid-catalyzed reactions, various two-dimensional and three-dimensional reaction representations (descriptors) were compared. Overall, the concatenated form of the extended connectivity fingerprints showed the best predictive capability for the two types of data sets: high-throughput experimental data and manually collected literature data sets. Furthermore, highlighting the substructure contribution to the prediction outcome was shown to be informative for guiding catalyst development.
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Affiliation(s)
- Ryosuke Asahara
- Graduate
School of Science and Technology, Nara Institute
of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan
| | - Tomoyuki Miyao
- Graduate
School of Science and Technology, Nara Institute
of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan
- Data
Science Center, Nara Institute of Science
and Technology, 8916-5
Takayama-cho, Ikoma, Nara 630-0192, Japan
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15
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Yang Z, Gao W. Applications of Machine Learning in Alloy Catalysts: Rational Selection and Future Development of Descriptors. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2106043. [PMID: 35229986 PMCID: PMC9036033 DOI: 10.1002/advs.202106043] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 02/02/2022] [Indexed: 05/28/2023]
Abstract
At present, alloys have broad application prospects in heterogeneous catalysis, due to their various catalytic active sites produced by their vast element combinations and complex geometric structures. However, it is the diverse variables of alloys that lead to the difficulty in understanding the structure-property relationship for conventional experimental and theoretical methods. Fortunately, machine learning methods are helpful to address the issue. Machine learning can not only deal with a large number of data rapidly, but also help establish the physical picture of reactions in multidimensional heterogeneous catalysis. The key challenge in machine learning is the exploration of suitable general descriptors to accurately describe various types of alloy catalysts, which help reasonably design catalysts and efficiently screen candidates. In this review, several kinds of machine learning methods commonly used in the design of alloy catalysts is introduced, and the applications of various reactivity descriptors corresponding to different alloy systems is summarized. Importantly, this work clarifies the existing understanding of physical picture of heterogeneous catalysis, and emphasize the significance of rational selection of universal descriptors. Finally, the development of heterogeneous catalytic descriptors for machine learning are presented.
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Affiliation(s)
- Ze Yang
- School of Materials Science and EngineeringJilin UniversityChangchun130022P. R. China
| | - Wang Gao
- School of Materials Science and EngineeringJilin UniversityChangchun130022P. R. China
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16
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Duan C, Nandy A, Kulik HJ. Machine Learning for the Discovery, Design, and Engineering of Materials. Annu Rev Chem Biomol Eng 2022; 13:405-429. [PMID: 35320698 DOI: 10.1146/annurev-chembioeng-092320-120230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Machine learning (ML) has become a part of the fabric of high-throughput screening and computational discovery of materials. Despite its increasingly central role, challenges remain in fully realizing the promise of ML. This is especially true for the practical acceleration of the engineering of robust materials and the development of design strategies that surpass trial and error or high-throughput screening alone. Depending on the quantity being predicted and the experimental data available, ML can either outperform physics-based modes, be used to accelerate such models, or be integrated with them to improve their performance. We cover recent advances in algorithms and in their application that are starting to make inroads toward (a) the discovery of new materials through large-scale enumerative screening, (b) the design of materials through identification of rules and principles that govern materials properties, and (c) the engineering of practical materials by satisfying multiple objectives. We conclude with opportunities for further advancement to realize ML as a widespread tool for practical computational materials design. Expected final online publication date for the Annual Review of Chemical and Biomolecular Engineering, Volume 13 is October 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Chenru Duan
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA; , , .,Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Aditya Nandy
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA; , , .,Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Heather J Kulik
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA; , ,
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17
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Mine S, Jing Y, Mukaiyama T, Takao M, Maeno Z, Shimizu KI, Takigawa I, Toyao T. Machine Learning Analysis of Literature Data on the Water Gas Shift Reaction Toward Extrapolative Prediction of Novel Catalysts. CHEM LETT 2022. [DOI: 10.1246/cl.210645] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Shinya Mine
- Institute for Catalysis, Hokkaido University, N-21, W-10, 1-5, Sapporo 001-0021, Japan
| | - Yuan Jing
- Institute for Catalysis, Hokkaido University, N-21, W-10, 1-5, Sapporo 001-0021, Japan
| | - Takumi Mukaiyama
- Institute for Catalysis, Hokkaido University, N-21, W-10, 1-5, Sapporo 001-0021, Japan
| | - Motoshi Takao
- Institute for Catalysis, Hokkaido University, N-21, W-10, 1-5, Sapporo 001-0021, Japan
| | - Zen Maeno
- Institute for Catalysis, Hokkaido University, N-21, W-10, 1-5, Sapporo 001-0021, Japan
| | - Ken-ichi Shimizu
- Institute for Catalysis, Hokkaido University, N-21, W-10, 1-5, Sapporo 001-0021, Japan
- Elements Strategy Initiative for Catalysts and Batteries, Kyoto University, Katsura, Kyoto 615-8520, Japan
| | - Ichigaku Takigawa
- RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, N-21, W-10, Sapporo 001-0021, Japan
| | - Takashi Toyao
- Institute for Catalysis, Hokkaido University, N-21, W-10, 1-5, Sapporo 001-0021, Japan
- Elements Strategy Initiative for Catalysts and Batteries, Kyoto University, Katsura, Kyoto 615-8520, Japan
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18
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Yada A, Sato K, Matsumura T, Ando Y, Nagata K, Ichinoseki S. Ensemble Learning Approach with LASSO for Predicting Catalytic Reaction Rates. Synlett 2021. [DOI: 10.1055/a-1304-4878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
AbstractThe prediction of the initial reaction rate in the tungsten-catalyzed epoxidation of alkenes by using a machine learning approach is demonstrated. The ensemble learning framework used in this study consists of random sampling with replacement from the training dataset, the construction of several predictive models (weak learners), and the combination of their outputs. This approach enables us to obtain a reasonable prediction model that avoids the problem of overfitting, even when analyzing a small dataset.
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Affiliation(s)
- Akira Yada
- Interdisciplinary Research Center for Catalytic Chemistry, National Institute of Advanced Industrial Science and Technology (AIST)
| | - Kazuhiko Sato
- Interdisciplinary Research Center for Catalytic Chemistry, National Institute of Advanced Industrial Science and Technology (AIST)
| | - Tarojiro Matsumura
- Research Center for Computational Design of Advanced Functional Materials, National Institute of Advanced Industrial Science and Technology (AIST)
| | - Yasunobu Ando
- Research Center for Computational Design of Advanced Functional Materials, National Institute of Advanced Industrial Science and Technology (AIST)
| | - Kenji Nagata
- Research and Services Division of Materials Data and Integrated System, National Institute for Materials Science (NIMS)
| | - Sakina Ichinoseki
- Interdisciplinary Research Center for Catalytic Chemistry, National Institute of Advanced Industrial Science and Technology (AIST)
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19
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Liang F, Zhang K, Zhang L, Zhang Y, Lei Y, Sun X. Recent Development of Electrocatalytic CO 2 Reduction Application to Energy Conversion. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2021; 17:e2100323. [PMID: 34151517 DOI: 10.1002/smll.202100323] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 04/12/2021] [Indexed: 06/13/2023]
Abstract
Carbon dioxide (CO2 ) emission has caused greenhouse gas pollution worldwide. Hence, strengthening CO2 recycling is necessary. CO2 electroreduction reaction (CRR) is recognized as a promising approach to utilize waste CO2 . Electrocatalysts in the CRR process play a critical role in determining the selectivity and activity of CRR. Different types of electrocatalysts are introduced in this review: noble metals and their derived compounds, transition metals and their derived compounds, organic polymer, and carbon-based materials, as well as their major products, Faradaic efficiency, current density, and onset potential. Furthermore, this paper overviews the recent progress of the following two major applications of CRR according to the different energy conversion methods: electricity generation and formation of valuable carbonaceous products. Considering electricity generation devices, the electrochemical properties of metal-CO2 batteries, including Li-CO2 , Na-CO2 , Al-CO2 , and Zn-CO2 batteries, are mainly summarized. Finally, different pathways of CO2 electroreduction to carbon-based fuels is presented, and their reaction mechanisms are illustrated. This review provides a clear and innovative insight into the entire reaction process of CRR, guiding the new electrocatalysts design, state-of-the-art analysis technique application, and reaction system innovation.
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Affiliation(s)
- Feng Liang
- Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming, 650093, China
- State Key Laboratory of Complex Nonferrous Metal Resources Clear Utilization, Kunming University of Science and Technology, Kunming, 650093, China
| | - Kaiwen Zhang
- Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming, 650093, China
| | - Lei Zhang
- Department of Mechanical and Materials Engineering, University of Western Ontario, London, Ontario, N6A 5B9, Canada
| | - Yingjie Zhang
- Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming, 650093, China
| | - Yong Lei
- Institute of Physics & IMN MacroNano (ZIK), Technical University of Ilmenau, 98693, Ilmenau, Germany
| | - Xueliang Sun
- Department of Mechanical and Materials Engineering, University of Western Ontario, London, Ontario, N6A 5B9, Canada
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20
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Weber JM, Guo Z, Zhang C, Schweidtmann AM, Lapkin AA. Chemical data intelligence for sustainable chemistry. Chem Soc Rev 2021; 50:12013-12036. [PMID: 34520507 DOI: 10.1039/d1cs00477h] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
This study highlights new opportunities for optimal reaction route selection from large chemical databases brought about by the rapid digitalisation of chemical data. The chemical industry requires a transformation towards more sustainable practices, eliminating its dependencies on fossil fuels and limiting its impact on the environment. However, identifying more sustainable process alternatives is, at present, a cumbersome, manual, iterative process, based on chemical intuition and modelling. We give a perspective on methods for automated discovery and assessment of competitive sustainable reaction routes based on renewable or waste feedstocks. Three key areas of transition are outlined and reviewed based on their state-of-the-art as well as bottlenecks: (i) data, (ii) evaluation metrics, and (iii) decision-making. We elucidate their synergies and interfaces since only together these areas can bring about the most benefit. The field of chemical data intelligence offers the opportunity to identify the inherently more sustainable reaction pathways and to identify opportunities for a circular chemical economy. Our review shows that at present the field of data brings about most bottlenecks, such as data completion and data linkage, but also offers the principal opportunity for advancement.
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Affiliation(s)
- Jana M Weber
- Department of Chemical Engineering and Biotechnology, University of Cambridge, West Cambridge Site, Philippa Fawcett Drive, Cambridge CB3 0AS, UK. .,Chemical Data Intelligence (CDI) Pte Ltd, Robinson Road, #02-00, 068898, Singapore
| | - Zhen Guo
- Chemical Data Intelligence (CDI) Pte Ltd, Robinson Road, #02-00, 068898, Singapore.,Cambridge Centre for Advanced Research and Education in Singapore, CARES Ltd. 1 CREATE Way, CREATE Tower #05-05, 138602, Singapore
| | - Chonghuan Zhang
- Department of Chemical Engineering and Biotechnology, University of Cambridge, West Cambridge Site, Philippa Fawcett Drive, Cambridge CB3 0AS, UK.
| | - Artur M Schweidtmann
- Department of Chemical Engineering, Delft University of Technology, Van der Maasweg 9, Delft 2629 HZ, The Netherlands
| | - Alexei A Lapkin
- Department of Chemical Engineering and Biotechnology, University of Cambridge, West Cambridge Site, Philippa Fawcett Drive, Cambridge CB3 0AS, UK. .,Chemical Data Intelligence (CDI) Pte Ltd, Robinson Road, #02-00, 068898, Singapore.,Cambridge Centre for Advanced Research and Education in Singapore, CARES Ltd. 1 CREATE Way, CREATE Tower #05-05, 138602, Singapore
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21
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Ebi T, Sen A, Dhital RN, Yamada YMA, Kaneko H. Design of Experimental Conditions with Machine Learning for Collaborative Organic Synthesis Reactions Using Transition-Metal Catalysts. ACS OMEGA 2021; 6:27578-27586. [PMID: 34693179 PMCID: PMC8529890 DOI: 10.1021/acsomega.1c04826] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 09/28/2021] [Indexed: 06/13/2023]
Abstract
To improve product yields in synthetic reactions, it is important to use appropriate catalysts. In this study, we used machine learning to design catalysts for a reaction system in which both Buchwald-Hartwig-type and Suzuki-Miyaura-type cross-coupling reactions proceed simultaneously. First, using an existing dataset, yield prediction models were constructed with machine learning between experimental conditions, including the substrate and catalyst and the yields of the two products. Seven methods for calculating both the substrate and catalyst descriptors were proposed, and the predictive ability of the yield prediction models was discussed in terms of the descriptors and machine learning methods. Then, the constructed models were used to predict the compound yields for new combinations of substrates and catalysts, and the predictions were experimentally validated with high reproducibility, confirming that machine learning can predict yields from experimental conditions with high accuracy. In addition, to design catalysts that will improve the yields in our dataset, we added datasets collected from scientific papers and designed catalyst ligands. The proposed catalyst candidates were tested in actual synthetic experiments, and the experimental results exceeded the existing yields.
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Affiliation(s)
- Tomoya Ebi
- Department
of Applied Chemistry, School of Science and Technology, Meiji University, 1-1-1 Higashi-Mita, Tama-ku, Kawasaki, Kanagawa 214-8571, Japan
| | - Abhijit Sen
- RIKEN
Center for Sustainable Resource Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Raghu N. Dhital
- RIKEN
Center for Sustainable Resource Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Yoichi M. A. Yamada
- RIKEN
Center for Sustainable Resource Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Hiromasa Kaneko
- Department
of Applied Chemistry, School of Science and Technology, Meiji University, 1-1-1 Higashi-Mita, Tama-ku, Kawasaki, Kanagawa 214-8571, Japan
- RIKEN
Center for Sustainable Resource Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
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22
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Sato A, Miyao T, Funatsu K. Prediction of Reaction Yield for Buchwald-Hartwig Cross-coupling Reactions Using Deep Learning. Mol Inform 2021; 41:e2100156. [PMID: 34585854 DOI: 10.1002/minf.202100156] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 09/12/2021] [Indexed: 11/09/2022]
Abstract
Chemical reaction yield is one of the most important factors for determining reaction conditions. Recently, several machine learning-based prediction models using high-throughput experiment (HTE) data sets were reported for the prediction of reaction yield. However, none of them were at a practical level in terms of predictive ability. In this study, we propose a message passing neural network (MPNN) model for chemical yield prediction, focusing on the Buchwald-Hartwig cross-coupling HTE data set. As an initial atom embedding in MPNN model, we propose to use the Mol2Vec feature vectors pre-trained using a large compound database. Predictive ability of the proposed model was higher than that of previously reported five models for the three out of five data sets. Moreover, visualization of important atoms based on self-attention mechanism was in favor of Mol2Vec as an atom embedding rather than other embeddings including previously employed simple representations.
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Affiliation(s)
- Akinori Sato
- Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara, 630-0192, Japan
| | - Tomoyuki Miyao
- Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara, 630-0192, Japan.,Data Science Center, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara, 630-0192, Japan
| | - Kimito Funatsu
- Data Science Center, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara, 630-0192, Japan
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23
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Nandy A, Duan C, Taylor MG, Liu F, Steeves AH, Kulik HJ. Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning. Chem Rev 2021; 121:9927-10000. [PMID: 34260198 DOI: 10.1021/acs.chemrev.1c00347] [Citation(s) in RCA: 70] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Transition-metal complexes are attractive targets for the design of catalysts and functional materials. The behavior of the metal-organic bond, while very tunable for achieving target properties, is challenging to predict and necessitates searching a wide and complex space to identify needles in haystacks for target applications. This review will focus on the techniques that make high-throughput search of transition-metal chemical space feasible for the discovery of complexes with desirable properties. The review will cover the development, promise, and limitations of "traditional" computational chemistry (i.e., force field, semiempirical, and density functional theory methods) as it pertains to data generation for inorganic molecular discovery. The review will also discuss the opportunities and limitations in leveraging experimental data sources. We will focus on how advances in statistical modeling, artificial intelligence, multiobjective optimization, and automation accelerate discovery of lead compounds and design rules. The overall objective of this review is to showcase how bringing together advances from diverse areas of computational chemistry and computer science have enabled the rapid uncovering of structure-property relationships in transition-metal chemistry. We aim to highlight how unique considerations in motifs of metal-organic bonding (e.g., variable spin and oxidation state, and bonding strength/nature) set them and their discovery apart from more commonly considered organic molecules. We will also highlight how uncertainty and relative data scarcity in transition-metal chemistry motivate specific developments in machine learning representations, model training, and in computational chemistry. Finally, we will conclude with an outlook of areas of opportunity for the accelerated discovery of transition-metal complexes.
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Affiliation(s)
- Aditya Nandy
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.,Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Chenru Duan
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.,Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Michael G Taylor
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Fang Liu
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Adam H Steeves
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Heather J Kulik
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
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24
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Varnek A, Zankov D, Polishchuk P, Madzhidov T. Multi-Instance Learning Approach to Predictive Modeling of Catalysts Enantioselectivity. Synlett 2021. [DOI: 10.1055/a-1553-0427] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
AbstractHere, we report an application of the multi-instance learning approach to predictive modeling of enantioselectivity of chiral catalysts. Catalysts were represented by ensembles of conformations encoded by the pmapper physicochemical descriptors capturing stereoconfiguration of the molecule. Each catalyzed chemical reaction was transformed to a condensed graph of reaction for which ISIDA fragment descriptors were generated. This approach does not require any conformations’ alignment and can potentially be used for a diverse set of catalysts bearing different scaffolds. Its efficiency has been demonstrated in predicting the selectivity of BINOL-derived phosphoric acid catalysts in asymmetric thiol addition to N-acylimines and benchmarked with previously reported models.
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Affiliation(s)
- A. Varnek
- Laboratory of Chemoinformatics, University of Strasbourg
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University
| | - D. Zankov
- Laboratory of Chemoinformatics, University of Strasbourg
- Laboratory of Chemoinformatics and Molecular Modeling, Kazan Federal University
| | - P. Polishchuk
- Institute of Molecular and Translational Medicine, Palacký University
| | - T. Madzhidov
- Laboratory of Chemoinformatics and Molecular Modeling, Kazan Federal University
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25
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Matsubara S. Digitization of Organic Synthesis — How Synthetic Organic Chemists Use AI Technology —. CHEM LETT 2021. [DOI: 10.1246/cl.200802] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Seijiro Matsubara
- Department of Material Chemistry, Graduate School of Engineering, Kyoto University, Kyodai-Katsura, Nishikyo, Nishikyo-ku, Kyoto 615-8501, Japan
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26
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Gallegos LC, Luchini G, St. John PC, Kim S, Paton RS. Importance of Engineered and Learned Molecular Representations in Predicting Organic Reactivity, Selectivity, and Chemical Properties. Acc Chem Res 2021; 54:827-836. [PMID: 33534534 DOI: 10.1021/acs.accounts.0c00745] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Machine-readable chemical structure representations are foundational in all attempts to harness machine learning for the prediction of reactivities, selectivities, and chemical properties directly from molecular structure. The featurization of discrete chemical structures into a continuous vector space is a critical phase undertaken before model selection, and the development of new ways to quantitatively encode molecules is an active area of research. In this Account, we highlight the application and suitability of different representations, from expert-guided "engineered" descriptors to automatically "learned" features, in different prediction tasks relevant to organic and organometallic chemistry, where differing amounts of training data are available. These tasks include statistical models of stereo- and enantioselectivity, thermochemistry, and kinetics developed using experimental and quantum chemical data.The use of expert-guided molecular descriptors provides an opportunity to incorporate chemical knowledge, domain expertise, and physical constraints into statistical modeling. In applications to stereoselective organic and organometallic catalysis, where data sets may be relatively small and 3D-geometries and conformations play an important role, mechanistically informed features can be used successfully to obtain predictive statistical models that are also chemically interpretable. We provide an overview of several recent applications of this approach to obtain quantitative models for reactivity and selectivity, where topological descriptors, quantum mechanical calculations of electronic and steric properties, along with conformational ensembles, all feature as essential ingredients of the molecular representations used.Alternatively, more flexible, general-purpose molecular representations such as attributed molecular graphs can be used with machine learning approaches to learn the complex relationship between a structure and prediction target. This approach has the potential to out-perform more traditional representation methods such as "hand-crafted" molecular descriptors, particularly as data set sizes grow. One area where this is particularly relevant is in the use of large sets of quantum mechanical data to train quantitative structure-property relationships. A general approach toward curating useful data sets and training highly accurate graph neural network models is discussed in the context of organic bond dissociation enthalpies, where this strategy outperforms regression using precomputed descriptors.Finally, we describe how graph neural network predictions can be incorporated into mechanistically informed statistical models of chemical reactivity and selectivity. Once trained, this approach avoids the expensive computational overhead associated with quantum mechanical calculations, while maintaining chemical interpretability. We illustrate examples for which fast predictions of bond dissociation enthalpy and of the identities of radicals formed through cleavage of a molecule's weakest bond are used in simple physical models of site-selectivity and reactivity.
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Affiliation(s)
- Liliana C. Gallegos
- Department of Chemistry, Colorado State University, Fort Collins, Colorado 80523, United States
| | - Guilian Luchini
- Department of Chemistry, Colorado State University, Fort Collins, Colorado 80523, United States
| | - Peter C. St. John
- Biosciences Center, National Renewable Energy Laboratory, 15103 Denver West Parkway, Golden, Colorado 80401, United States
| | - Seonah Kim
- Biosciences Center, National Renewable Energy Laboratory, 15103 Denver West Parkway, Golden, Colorado 80401, United States
| | - Robert S. Paton
- Department of Chemistry, Colorado State University, Fort Collins, Colorado 80523, United States
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27
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Ishimoto T, Tsukada S, Wakitani S, Sato K, Saito D, Nakanishi Y, Takase S, Hamada T, Ohshita J, Kai H. Model-based research toward design of innovative materials: molecular weight prediction of bridged polysilsesquioxanes. RSC Adv 2020; 10:28595-28602. [PMID: 35520051 PMCID: PMC9055803 DOI: 10.1039/d0ra02909b] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 07/10/2020] [Indexed: 11/21/2022] Open
Abstract
Toward the design and manipulation of innovative materials, we propose a new concept called “model-based research (MBR)”. In MBR, measurable physical and chemical properties of materials are mathematically modelled by explanatory parameters obtained by computer simulation from an atomistic point of view. To demonstrate the potential of MBR, we modelled the molecular weights of a series of polysilsesquioxanes with respect to the H2O/silane molar ratio employed for the polymerization of monomers bis(triethoxysilyl)methane, ethane, ethylene, and acetylene (BTES-M, -E1, -E2, and -E3), as an example. The equation y = axn well reproduced the behaviour of the molecular weights of the BTES series, in which a and n were obtained using the calculated molecular parameters for monomers as the explanatory parameters. Detailed understanding and discussion were theoretically possible on the basis of the mathematical model. We predicted the molecular weights of polymers that would be obtained from monomers BTES-P and BTES-Ph with C3H6 and C6H4 as the spacer, respectively, using the mathematical model. Experimental validation of these polymers clearly showed the possibility of qualitative categorization. Our proposed concept, MBR, is a powerful tool to analyse materials science toward innovative materials design. Modeling and prediction of molecular weight by MBR.![]()
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Affiliation(s)
- Takayoshi Ishimoto
- Advanced Materials Laboratory, Advanced Automotive Research Collaborative Laboratory, Graduate School of Engineering, Hiroshima University 1-4-1 Kagamiyama Higashi-Hiroshima Hiroshima 739-8527 Japan .,Graduate School of Nanobioscience, Yokohama City University 22-2 Seto, Kanazawa-ku Yokohama 236-0027 Japan
| | - Satoru Tsukada
- Advanced Materials Laboratory, Advanced Automotive Research Collaborative Laboratory, Graduate School of Engineering, Hiroshima University 1-4-1 Kagamiyama Higashi-Hiroshima Hiroshima 739-8527 Japan
| | - Shin Wakitani
- Fundamentals of Model-Based Development, Graduate School of Advanced Science and Engineering, Hiroshima University 1-4-1 Kagamiyama Higashi-Hiroshima Hiroshima 739-8527 Japan
| | - Kenji Sato
- Advanced Materials Laboratory, Advanced Automotive Research Collaborative Laboratory, Graduate School of Engineering, Hiroshima University 1-4-1 Kagamiyama Higashi-Hiroshima Hiroshima 739-8527 Japan
| | - Daiki Saito
- Department of Applied Chemistry, Graduate School of Advanced Science and Engineering, Hiroshima University 1-4-1 Kagamiyama Higashi-Hiroshima Hiroshima 739-8527 Japan
| | - Yuki Nakanishi
- Department of Applied Chemistry, Graduate School of Advanced Science and Engineering, Hiroshima University 1-4-1 Kagamiyama Higashi-Hiroshima Hiroshima 739-8527 Japan
| | - Sakino Takase
- Department of Applied Chemistry, Graduate School of Advanced Science and Engineering, Hiroshima University 1-4-1 Kagamiyama Higashi-Hiroshima Hiroshima 739-8527 Japan
| | - Takashi Hamada
- Advanced Materials Laboratory, Advanced Automotive Research Collaborative Laboratory, Graduate School of Engineering, Hiroshima University 1-4-1 Kagamiyama Higashi-Hiroshima Hiroshima 739-8527 Japan .,Department of Applied Chemistry, Graduate School of Advanced Science and Engineering, Hiroshima University 1-4-1 Kagamiyama Higashi-Hiroshima Hiroshima 739-8527 Japan
| | - Joji Ohshita
- Advanced Materials Laboratory, Advanced Automotive Research Collaborative Laboratory, Graduate School of Engineering, Hiroshima University 1-4-1 Kagamiyama Higashi-Hiroshima Hiroshima 739-8527 Japan .,Department of Applied Chemistry, Graduate School of Advanced Science and Engineering, Hiroshima University 1-4-1 Kagamiyama Higashi-Hiroshima Hiroshima 739-8527 Japan.,Division of Materials Model-Based Research, Digital Monozukuri (Manufacturing) Education and Research Center, Hiroshima University Higashi-Hiroshima 739-0046 Japan
| | - Hiroyuki Kai
- Advanced Materials Laboratory, Advanced Automotive Research Collaborative Laboratory, Graduate School of Engineering, Hiroshima University 1-4-1 Kagamiyama Higashi-Hiroshima Hiroshima 739-8527 Japan
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Interactive-quantum-chemical-descriptors enabling accurate prediction of an activation energy through machine learning. POLYMER 2020. [DOI: 10.1016/j.polymer.2020.122738] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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Fujinami M, Seino J, Nakai H. Quantum Chemical Reaction Prediction Method Based on Machine Learning. BULLETIN OF THE CHEMICAL SOCIETY OF JAPAN 2020. [DOI: 10.1246/bcsj.20200017] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Mikito Fujinami
- Department of Chemistry and Biochemistry, School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
| | - Junji Seino
- Waseda Research Institute for Science and Engineering (WISE), Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
- PRESTO, Japan Science and Technology Agency (JST), 4-1-8 Honcho, Kawaguchi, Saitama 332-0012, Japan
| | - Hiromi Nakai
- Department of Chemistry and Biochemistry, School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
- Waseda Research Institute for Science and Engineering (WISE), Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
- Elements Strategy Initiative for Catalysts and Batteries (ESICB), Kyoto University, Katsura, Kyoto, Japan
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30
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Fu Z, Li X, Wang Z, Li Z, Liu X, Wu X, Zhao J, Ding X, Wan X, Zhong F, Wang D, Luo X, Chen K, Liu H, Wang J, Jiang H, Zheng M. Optimizing chemical reaction conditions using deep learning: a case study for the Suzuki–Miyaura cross-coupling reaction. Org Chem Front 2020. [DOI: 10.1039/d0qo00544d] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Deep learning was used to optimize chemical reactions with the quantum mechanical properties of chemical contexts and reaction conditions as inputs. The trained deep learning model determines optimal reaction conditions by in silico exploration of accessible reaction space.
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31
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Toyao T, Maeno Z, Takakusagi S, Kamachi T, Takigawa I, Shimizu KI. Machine Learning for Catalysis Informatics: Recent Applications and Prospects. ACS Catal 2019. [DOI: 10.1021/acscatal.9b04186] [Citation(s) in RCA: 189] [Impact Index Per Article: 37.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Takashi Toyao
- Institute for Catalysis, Hokkaido University, N-21, W-10, Sapporo 001-0021, Japan
- Elements Strategy Initiative for Catalysts and Batteries, Kyoto University, Katsura, Kyoto 615-8520, Japan
| | - Zen Maeno
- Institute for Catalysis, Hokkaido University, N-21, W-10, Sapporo 001-0021, Japan
| | - Satoru Takakusagi
- Institute for Catalysis, Hokkaido University, N-21, W-10, Sapporo 001-0021, Japan
| | - Takashi Kamachi
- Elements Strategy Initiative for Catalysts and Batteries, Kyoto University, Katsura, Kyoto 615-8520, Japan
- Department of Life, Environment and Materials Science, Fukuoka Institute of Technology, 3-30-1Wajiro-Higashi, Higashi-ku, Fukuoka 811-0295, Japan
| | - Ichigaku Takigawa
- RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Kita 21 Nishi 10, Kita-ku, Sapporo, Hokkaido 001-0021, Japan
| | - Ken-ichi Shimizu
- Institute for Catalysis, Hokkaido University, N-21, W-10, Sapporo 001-0021, Japan
- Elements Strategy Initiative for Catalysts and Batteries, Kyoto University, Katsura, Kyoto 615-8520, Japan
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32
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Freeze JG, Kelly HR, Batista VS. Search for Catalysts by Inverse Design: Artificial Intelligence, Mountain Climbers, and Alchemists. Chem Rev 2019; 119:6595-6612. [PMID: 31059236 DOI: 10.1021/acs.chemrev.8b00759] [Citation(s) in RCA: 99] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
In silico catalyst design is a grand challenge of chemistry. Traditional computational approaches have been limited by the need to compute properties for an intractably large number of possible catalysts. Recently, inverse design methods have emerged, starting from a desired property and optimizing a corresponding chemical structure. Techniques used for exploring chemical space include gradient-based optimization, alchemical transformations, and machine learning. Though the application of these methods to catalysis is in its early stages, further development will allow for robust computational catalyst design. This review provides an overview of the evolution of inverse design approaches and their relevance to catalysis. The strengths and limitations of existing techniques are highlighted, and suggestions for future research are provided.
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Affiliation(s)
- Jessica G Freeze
- Department of Chemistry , Yale University , New Haven , Connecticut 06520 , United States.,Energy Sciences Institute , Yale University , West Haven , Connecticut 06516 , United States
| | - H Ray Kelly
- Department of Chemistry , Yale University , New Haven , Connecticut 06520 , United States.,Energy Sciences Institute , Yale University , West Haven , Connecticut 06516 , United States
| | - Victor S Batista
- Energy Sciences Institute , Yale University , West Haven , Connecticut 06516 , United States.,Department of Chemistry , Yale University , P.O. Box 208107 , New Haven , Connecticut 06520 , United States
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Abstract
Ligands, especially phosphines and carbenes, can play a key role in modifying and controlling homogeneous organometallic catalysts, and they often provide a convenient approach to fine-tuning the performance of known catalysts. The measurable outcomes of such catalyst modifications (yields, rates, selectivity) can be set into context by establishing their relationship to steric and electronic descriptors of ligand properties, and such models can guide the discovery, optimization, and design of catalysts. In this review we present a survey of calculated ligand descriptors, with a particular focus on homogeneous organometallic catalysis. A range of different approaches to calculating steric and electronic parameters are set out and compared, and we have collected descriptors for a range of representative ligand sets, including 30 monodentate phosphorus(III) donor ligands, 23 bidentate P,P-donor ligands, and 30 carbenes, with a view to providing a useful resource for analysis to practitioners. In addition, several case studies of applications of such descriptors, covering both maps and models, have been reviewed, illustrating how descriptor-led studies of catalysis can inform experiments and highlighting good practice for model comparison and evaluation.
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Affiliation(s)
- Derek J Durand
- School of Chemistry , University of Bristol , Cantock's Close , Bristol BS8 1TS , U.K
| | - Natalie Fey
- School of Chemistry , University of Bristol , Cantock's Close , Bristol BS8 1TS , U.K
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34
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Lakuntza O, Besora M, Maseras F. Searching for Hidden Descriptors in the Metal–Ligand Bond through Statistical Analysis of Density Functional Theory (DFT) Results. Inorg Chem 2018; 57:14660-14670. [DOI: 10.1021/acs.inorgchem.8b02372] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Oier Lakuntza
- Institute of Chemical Research of Catalonia (ICIQ), The Barcelona Institute of Science and Technology, Avgda. Països Catalans, 16, 43007 Tarragona, Catalonia, Spain
| | - Maria Besora
- Institute of Chemical Research of Catalonia (ICIQ), The Barcelona Institute of Science and Technology, Avgda. Països Catalans, 16, 43007 Tarragona, Catalonia, Spain
| | - Feliu Maseras
- Institute of Chemical Research of Catalonia (ICIQ), The Barcelona Institute of Science and Technology, Avgda. Països Catalans, 16, 43007 Tarragona, Catalonia, Spain
- Department de Química, Universitat Autònoma de Barcelona, 08193 Bellaterra, Catalonia, Spain
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35
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Medford AJ, Kunz MR, Ewing SM, Borders T, Fushimi R. Extracting Knowledge from Data through Catalysis Informatics. ACS Catal 2018. [DOI: 10.1021/acscatal.8b01708] [Citation(s) in RCA: 128] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Andrew J. Medford
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30318 United States
| | - M. Ross Kunz
- Biological and Chemical Processing Department, Energy and Environmental Science and Technology, Idaho National Laboratory, P.O. Box 1625, Idaho Falls, Idaho 83415, United States
| | - Sarah M. Ewing
- Biological and Chemical Processing Department, Energy and Environmental Science and Technology, Idaho National Laboratory, P.O. Box 1625, Idaho Falls, Idaho 83415, United States
| | - Tammie Borders
- Biological and Chemical Processing Department, Energy and Environmental Science and Technology, Idaho National Laboratory, P.O. Box 1625, Idaho Falls, Idaho 83415, United States
| | - Rebecca Fushimi
- Biological and Chemical Processing Department, Energy and Environmental Science and Technology, Idaho National Laboratory, P.O. Box 1625, Idaho Falls, Idaho 83415, United States
- Center for Advanced Energy Studies, 995 University Boulevard, Idaho Falls, Idaho 83401, United States
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