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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Wang JY, Stevens JM, Kariofillis SK, Tom MJ, Golden DL, Li J, Tabora JE, Parasram M, Shields BJ, Primer DN, Hao B, Del Valle D, DiSomma S, Furman A, Zipp GG, Melnikov S, Paulson J, Doyle AG. Identifying general reaction conditions by bandit optimization. Nature 2024; 626:1025-1033. [PMID: 38418912 DOI: 10.1038/s41586-024-07021-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 01/03/2024] [Indexed: 03/02/2024]
Abstract
Reaction conditions that are generally applicable to a wide variety of substrates are highly desired, especially in the pharmaceutical and chemical industries1-6. Although many approaches are available to evaluate the general applicability of developed conditions, a universal approach to efficiently discover these conditions during optimizations is rare. Here we report the design, implementation and application of reinforcement learning bandit optimization models7-10 to identify generally applicable conditions by efficient condition sampling and evaluation of experimental feedback. Performance benchmarking on existing datasets statistically showed high accuracies for identifying general conditions, with up to 31% improvement over baselines that mimic state-of-the-art optimization approaches. A palladium-catalysed imidazole C-H arylation reaction, an aniline amide coupling reaction and a phenol alkylation reaction were investigated experimentally to evaluate use cases and functionalities of the bandit optimization model in practice. In all three cases, the reaction conditions that were most generally applicable yet not well studied for the respective reaction were identified after surveying less than 15% of the expert-designed reaction space.
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Affiliation(s)
- Jason Y Wang
- Department of Chemistry, Princeton University, Princeton, NJ, USA
- Department of Chemistry and Biochemistry, University of California, Los Angeles, CA, USA
| | - Jason M Stevens
- Chemical Process Development, Bristol Myers Squibb, Summit, NJ, USA
| | - Stavros K Kariofillis
- Department of Chemistry, Princeton University, Princeton, NJ, USA
- Department of Chemistry and Biochemistry, University of California, Los Angeles, CA, USA
- Department of Chemistry, Columbia University, New York, NY, USA
| | - Mai-Jan Tom
- Department of Chemistry and Biochemistry, University of California, Los Angeles, CA, USA
| | - Dung L Golden
- Chemical Process Development, Bristol Myers Squibb, Summit, NJ, USA
| | - Jun Li
- Chemical Process Development, Bristol Myers Squibb, New Brunswick, NJ, USA
| | - Jose E Tabora
- Chemical Process Development, Bristol Myers Squibb, New Brunswick, NJ, USA
| | - Marvin Parasram
- Department of Chemistry, Princeton University, Princeton, NJ, USA
- Department of Chemistry, New York University, New York, NY, USA
| | - Benjamin J Shields
- Department of Chemistry, Princeton University, Princeton, NJ, USA
- Molecular Structure and Design, Bristol Myers Squibb, Cambridge, MA, USA
| | - David N Primer
- Chemical Process Development, Bristol Myers Squibb, Summit, NJ, USA
- Loxo Oncology at Lilly, Louisville, CO, USA
| | - Bo Hao
- Janssen Research and Development, Spring House, PA, USA
| | - David Del Valle
- Chemical Process Development, Bristol Myers Squibb, New Brunswick, NJ, USA
| | - Stacey DiSomma
- Chemical Process Development, Bristol Myers Squibb, New Brunswick, NJ, USA
| | - Ariel Furman
- Chemical Process Development, Bristol Myers Squibb, New Brunswick, NJ, USA
| | - G Greg Zipp
- Discovery Synthesis, Bristol Myers Squibb, Princeton, NJ, USA
| | | | - James Paulson
- Chemical Process Development, Bristol Myers Squibb, New Brunswick, NJ, USA
| | - Abigail G Doyle
- Department of Chemistry, Princeton University, Princeton, NJ, USA.
- Department of Chemistry and Biochemistry, University of California, Los Angeles, CA, USA.
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3
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Wang X, Hsieh CY, Yin X, Wang J, Li Y, Deng Y, Jiang D, Wu Z, Du H, Chen H, Li Y, Liu H, Wang Y, Luo P, Hou T, Yao X. Generic Interpretable Reaction Condition Predictions with Open Reaction Condition Datasets and Unsupervised Learning of Reaction Center. RESEARCH (WASHINGTON, D.C.) 2023; 6:0231. [PMID: 37849643 PMCID: PMC10578430 DOI: 10.34133/research.0231] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 08/29/2023] [Indexed: 10/19/2023]
Abstract
Effective synthesis planning powered by deep learning (DL) can significantly accelerate the discovery of new drugs and materials. However, most DL-assisted synthesis planning methods offer either none or very limited capability to recommend suitable reaction conditions (RCs) for their reaction predictions. Currently, the prediction of RCs with a DL framework is hindered by several factors, including: (a) lack of a standardized dataset for benchmarking, (b) lack of a general prediction model with powerful representation, and (c) lack of interpretability. To address these issues, we first created 2 standardized RC datasets covering a broad range of reaction classes and then proposed a powerful and interpretable Transformer-based RC predictor named Parrot. Through careful design of the model architecture, pretraining method, and training strategy, Parrot improved the overall top-3 prediction accuracy on catalysis, solvents, and other reagents by as much as 13.44%, compared to the best previous model on a newly curated dataset. Additionally, the mean absolute error of the predicted temperatures was reduced by about 4 °C. Furthermore, Parrot manifests strong generalization capacity with superior cross-chemical-space prediction accuracy. Attention analysis indicates that Parrot effectively captures crucial chemical information and exhibits a high level of interpretability in the prediction of RCs. The proposed model Parrot exemplifies how modern neural network architecture when appropriately pretrained can be versatile in making reliable, generalizable, and interpretable recommendation for RCs even when the underlying training dataset may still be limited in diversity.
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Affiliation(s)
- Xiaorui Wang
- Dr. Neher’s Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health,
Macau University of Science and Technology, Macao, 999078, China
- CarbonSilicon AI Technology Co.,
Ltd, Hangzhou, Zhejiang310018, China
| | - Chang-Yu Hsieh
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences,
Zhejiang University, Hangzhou, 310058, China
| | - Xiaodan Yin
- Dr. Neher’s Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health,
Macau University of Science and Technology, Macao, 999078, China
- CarbonSilicon AI Technology Co.,
Ltd, Hangzhou, Zhejiang310018, China
| | - Jike Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences,
Zhejiang University, Hangzhou, 310058, China
- CarbonSilicon AI Technology Co.,
Ltd, Hangzhou, Zhejiang310018, China
| | - Yuquan Li
- College of Chemistry and Chemical Engineering,
Lanzhou University, Lanzhou, 730000, China
| | - Yafeng Deng
- CarbonSilicon AI Technology Co.,
Ltd, Hangzhou, Zhejiang310018, China
| | - Dejun Jiang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences,
Zhejiang University, Hangzhou, 310058, China
- CarbonSilicon AI Technology Co.,
Ltd, Hangzhou, Zhejiang310018, China
| | - Zhenxing Wu
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences,
Zhejiang University, Hangzhou, 310058, China
- CarbonSilicon AI Technology Co.,
Ltd, Hangzhou, Zhejiang310018, China
| | - Hongyan Du
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences,
Zhejiang University, Hangzhou, 310058, China
| | - Hongming Chen
- Center of Chemistry and Chemical Biology,
Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou 510530, China
| | - Yun Li
- College of Chemistry and Chemical Engineering,
Lanzhou University, Lanzhou, 730000, China
| | - Huanxiang Liu
- Faculty of Applied Sciences,
Macao Polytechnic University, Macao, 999078, China
| | - Yuwei Wang
- College of Pharmacy,
Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, 712044, China
| | - Pei Luo
- Dr. Neher’s Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health,
Macau University of Science and Technology, Macao, 999078, China
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences,
Zhejiang University, Hangzhou, 310058, China
| | - Xiaojun Yao
- Faculty of Applied Sciences,
Macao Polytechnic University, Macao, 999078, China
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4
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Rinehart NI, Saunthwal RK, Wellauer J, Zahrt AF, Schlemper L, Shved AS, Bigler R, Fantasia S, Denmark SE. A machine-learning tool to predict substrate-adaptive conditions for Pd-catalyzed C-N couplings. Science 2023; 381:965-972. [PMID: 37651532 DOI: 10.1126/science.adg2114] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 08/01/2023] [Indexed: 09/02/2023]
Abstract
Machine-learning methods have great potential to accelerate the identification of reaction conditions for chemical transformations. A tool that gives substrate-adaptive conditions for palladium (Pd)-catalyzed carbon-nitrogen (C-N) couplings is presented. The design and construction of this tool required the generation of an experimental dataset that explores a diverse network of reactant pairings across a set of reaction conditions. A large scope of C-N couplings was actively learned by neural network models by using a systematic process to design experiments. The models showed good performance in experimental validation: Ten products were isolated in more than 85% yield from a range of couplings with out-of-sample reactants designed to challenge the models. Importantly, the developed workflow continually improves the prediction capability of the tool as the corpus of data grows.
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Affiliation(s)
- N Ian Rinehart
- Roger Adams Laboratory, Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Rakesh K Saunthwal
- Roger Adams Laboratory, Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Joël Wellauer
- Pharmaceutical Division, Synthetic Molecules Technical Development, Process Chemistry and Catalysis, F. Hoffmann-La Roche, Ltd., Basel, Switzerland
| | - Andrew F Zahrt
- Roger Adams Laboratory, Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Lukas Schlemper
- Pharmaceutical Division, Synthetic Molecules Technical Development, Process Chemistry and Catalysis, F. Hoffmann-La Roche, Ltd., Basel, Switzerland
| | - Alexander S Shved
- Roger Adams Laboratory, Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Raphael Bigler
- Pharmaceutical Division, Synthetic Molecules Technical Development, Process Chemistry and Catalysis, F. Hoffmann-La Roche, Ltd., Basel, Switzerland
| | - Serena Fantasia
- Pharmaceutical Division, Synthetic Molecules Technical Development, Process Chemistry and Catalysis, F. Hoffmann-La Roche, Ltd., Basel, Switzerland
| | - Scott E Denmark
- Roger Adams Laboratory, Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
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5
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Shim E, Tewari A, Cernak T, Zimmerman PM. Machine Learning Strategies for Reaction Development: Toward the Low-Data Limit. J Chem Inf Model 2023; 63:3659-3668. [PMID: 37312524 PMCID: PMC11163943 DOI: 10.1021/acs.jcim.3c00577] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Machine learning models are increasingly being utilized to predict outcomes of organic chemical reactions. A large amount of reaction data is used to train these models, which is in stark contrast to how expert chemists discover and develop new reactions by leveraging information from a small number of relevant transformations. Transfer learning and active learning are two strategies that can operate in low-data situations, which may help fill this gap and promote the use of machine learning for tackling real-world challenges in organic synthesis. This Perspective introduces active and transfer learning and connects these to potential opportunities and directions for further research, especially in the area of prospective development of chemical transformations.
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Affiliation(s)
- Eunjae Shim
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Ambuj Tewari
- Department of Statistics, University of Michigan, Ann Arbor, Michigan 48109, United States
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Tim Cernak
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
- Department of Medicinal Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Paul M Zimmerman
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
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6
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Faurschou NV, Taaning RH, Pedersen CM. Substrate specific closed-loop optimization of carbohydrate protective group chemistry using Bayesian optimization and transfer learning. Chem Sci 2023; 14:6319-6329. [PMID: 37325141 PMCID: PMC10266441 DOI: 10.1039/d3sc01261a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 05/12/2023] [Indexed: 06/17/2023] Open
Abstract
A new way of performing reaction optimization within carbohydrate chemistry is presented. This is done by performing closed-loop optimization of regioselective benzoylation of unprotected glycosides using Bayesian optimization. Both 6-O-monobenzoylations and 3,6-O-dibenzoylations of three different monosaccharides are optimized. A novel transfer learning approach, where data from previous optimizations of different substrates is used to speed up the optimizations, has also been developed. The optimal conditions found by the Bayesian optimization algorithm provide new insight into substrate specificity, as the conditions found are significantly different. In most cases, the optimal conditions include Et3N and benzoic anhydride, a new reagent combination for these reactions, discovered by the algorithm, demonstrating the power of this concept to widen the chemical space. Further, the developed procedures include ambient conditions and short reaction times.
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7
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Capaldo L, Wen Z, Noël T. A field guide to flow chemistry for synthetic organic chemists. Chem Sci 2023; 14:4230-4247. [PMID: 37123197 PMCID: PMC10132167 DOI: 10.1039/d3sc00992k] [Citation(s) in RCA: 34] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 03/15/2023] [Indexed: 03/17/2023] Open
Abstract
Flow chemistry has unlocked a world of possibilities for the synthetic community, but the idea that it is a mysterious "black box" needs to go. In this review, we show that several of the benefits of microreactor technology can be exploited to push the boundaries in organic synthesis and to unleash unique reactivity and selectivity. By "lifting the veil" on some of the governing principles behind the observed trends, we hope that this review will serve as a useful field guide for those interested in diving into flow chemistry.
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Affiliation(s)
- Luca Capaldo
- Flow Chemistry Group, Van 't Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam 1098 XH Amsterdam The Netherlands
| | - Zhenghui Wen
- Flow Chemistry Group, Van 't Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam 1098 XH Amsterdam The Netherlands
| | - Timothy Noël
- Flow Chemistry Group, Van 't Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam 1098 XH Amsterdam The Netherlands
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8
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Chen Y, Ou Y, Zheng P, Huang Y, Ge F, Dral PO. Benchmark of general-purpose machine learning-based quantum mechanical method AIQM1 on reaction barrier heights. J Chem Phys 2023; 158:074103. [PMID: 36813722 DOI: 10.1063/5.0137101] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Artificial intelligence-enhanced quantum mechanical method 1 (AIQM1) is a general-purpose method that was shown to achieve high accuracy for many applications with a speed close to its baseline semiempirical quantum mechanical (SQM) method ODM2*. Here, we evaluate the hitherto unknown performance of out-of-the-box AIQM1 without any refitting for reaction barrier heights on eight datasets, including a total of ∼24 thousand reactions. This evaluation shows that AIQM1's accuracy strongly depends on the type of transition state and ranges from excellent for rotation barriers to poor for, e.g., pericyclic reactions. AIQM1 clearly outperforms its baseline ODM2* method and, even more so, a popular universal potential, ANI-1ccx. Overall, however, AIQM1 accuracy largely remains similar to SQM methods (and B3LYP/6-31G* for most reaction types) suggesting that it is desirable to focus on improving AIQM1 performance for barrier heights in the future. We also show that the built-in uncertainty quantification helps in identifying confident predictions. The accuracy of confident AIQM1 predictions is approaching the level of popular density functional theory methods for most reaction types. Encouragingly, AIQM1 is rather robust for transition state optimizations, even for the type of reactions it struggles with the most. Single-point calculations with high-level methods on AIQM1-optimized geometries can be used to significantly improve barrier heights, which cannot be said for its baseline ODM2* method.
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Affiliation(s)
- Yuxinxin Chen
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Department of Chemistry, and College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Yanchi Ou
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Department of Chemistry, and College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Peikun Zheng
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Department of Chemistry, and College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Yaohuang Huang
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Department of Chemistry, and College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Fuchun Ge
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Department of Chemistry, and College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Pavlo O Dral
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Department of Chemistry, and College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
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9
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Singh S, Sunoj RB. Molecular Machine Learning for Chemical Catalysis: Prospects and Challenges. Acc Chem Res 2023; 56:402-412. [PMID: 36715248 DOI: 10.1021/acs.accounts.2c00801] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
ConspectusIn the domain of reaction development, one aims to obtain higher efficacies as measured in terms of yield and/or selectivities. During the empirical cycles, an admixture of outcomes from low to high yields/selectivities is expected. While it is not easy to identify all of the factors that might impact the reaction efficiency, complex and nonlinear dependence on the nature of reactants, catalysts, solvents, etc. is quite likely. Developmental stages of newer reactions would typically offer a few hundreds of samples with variations in participating molecules and/or reaction conditions. These "observations" and their "output" can be harnessed as valuable labeled data for developing molecular machine learning (ML) models. Once a robust ML model is built for a specific reaction under development, it can predict the reaction outcome for any new choice of substrates/catalyst in a few seconds/minutes and thus can expedite the identification of promising candidates for experimental validation. Recent years have witnessed impressive applications of ML in the molecular world, most of them aimed at predicting important chemical or biological properties. We believe that an integration of effective ML workflows can be made richly beneficial to reaction discovery.As with any new technology, direct adaptation of ML as used in well-developed domains, such as natural language processing (NLP) and image recognition, is unlikely to succeed in reaction discovery. Some of the challenges stem from ineffective featurization of the molecular space, unavailability of quality data and its distribution, in making the right choice of ML model and its technically robust deployment. It shall be noted that there is no universal ML model suitable for an inherently high-dimensional problem such as chemical reactions. Given these backgrounds, rendering ML tools conducive for reactions is an exciting as well as challenging endeavor at the same time. With the increased availability of efficient ML algorithms, we focused on tapping their potential for small-data reaction discovery (a few hundreds to thousands of samples).In this Account, we describe both feature engineering and feature learning approaches for molecular ML as applied to diverse reactions of high contemporary interest. Among these, catalytic asymmetric hydrogenation of imines/alkenes, β-C(sp3)-H bond functionalization, and relay Heck reaction employed a feature engineering approach using the quantum-chemically derived physical organic descriptors as the molecular features─all designed to predict the enantioselectivity. The selection of molecular features to customize it for a reaction of interest is described, along with emphasizing the chemical insights that could be gathered through the use of such features. Feature learning methods for predicting the yield of Buchwald-Hartwig cross-coupling, deoxyfluorination of alcohols, and enantioselectivity of N,S-acetal formation are found to offer excellent predictions. We propose a transfer learning protocol, wherein an ML model such as a language model is trained on a large number of molecules (105-106) and fine-tuned on a focused library of target task reactions, as an effective alternative for small-data reaction discovery (102-103 reactions). The exploitation of deep neural network latent space as a method for generative tasks to identify useful substrates for a reaction is demonstrated as a promising strategy.
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Affiliation(s)
- Sukriti Singh
- Department of Chemistry, Indian Institute of Technology Bombay, Mumbai 400076, India
| | - Raghavan B Sunoj
- Department of Chemistry, Indian Institute of Technology Bombay, Mumbai 400076, India.,Centre for Machine Intelligence and Data Science, Indian Institute of Technology Bombay, Mumbai 400076, India
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10
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Seifrid M, Pollice R, Aguilar-Granda A, Morgan Chan Z, Hotta K, Ser CT, Vestfrid J, Wu TC, Aspuru-Guzik A. Autonomous Chemical Experiments: Challenges and Perspectives on Establishing a Self-Driving Lab. Acc Chem Res 2022; 55:2454-2466. [PMID: 35948428 PMCID: PMC9454899 DOI: 10.1021/acs.accounts.2c00220] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Indexed: 01/19/2023]
Abstract
We must accelerate the pace at which we make technological advancements to address climate change and disease risks worldwide. This swifter pace of discovery requires faster research and development cycles enabled by better integration between hypothesis generation, design, experimentation, and data analysis. Typical research cycles take months to years. However, data-driven automated laboratories, or self-driving laboratories, can significantly accelerate molecular and materials discovery. Recently, substantial advancements have been made in the areas of machine learning and optimization algorithms that have allowed researchers to extract valuable knowledge from multidimensional data sets. Machine learning models can be trained on large data sets from the literature or databases, but their performance can often be hampered by a lack of negative results or metadata. In contrast, data generated by self-driving laboratories can be information-rich, containing precise details of the experimental conditions and metadata. Consequently, much larger amounts of high-quality data are gathered in self-driving laboratories. When placed in open repositories, this data can be used by the research community to reproduce experiments, for more in-depth analysis, or as the basis for further investigation. Accordingly, high-quality open data sets will increase the accessibility and reproducibility of science, which is sorely needed.In this Account, we describe our efforts to build a self-driving lab for the development of a new class of materials: organic semiconductor lasers (OSLs). Since they have only recently been demonstrated, little is known about the molecular and material design rules for thin-film, electrically-pumped OSL devices as compared to other technologies such as organic light-emitting diodes or organic photovoltaics. To realize high-performing OSL materials, we are developing a flexible system for automated synthesis via iterative Suzuki-Miyaura cross-coupling reactions. This automated synthesis platform is directly coupled to the analysis and purification capabilities. Subsequently, the molecules of interest can be transferred to an optical characterization setup. We are currently limited to optical measurements of the OSL molecules in solution. However, material properties are ultimately most important in the solid state (e.g., as a thin-film device). To that end and for a different scientific goal, we are developing a self-driving lab for inorganic thin-film materials focused on the oxygen evolution reaction.While the future of self-driving laboratories is very promising, numerous challenges still need to be overcome. These challenges can be split into cognition and motor function. Generally, the cognitive challenges are related to optimization with constraints or unexpected outcomes for which general algorithmic solutions have yet to be developed. A more practical challenge that could be resolved in the near future is that of software control and integration because few instrument manufacturers design their products with self-driving laboratories in mind. Challenges in motor function are largely related to handling heterogeneous systems, such as dispensing solids or performing extractions. As a result, it is critical to understand that adapting experimental procedures that were designed for human experimenters is not as simple as transferring those same actions to an automated system, and there may be more efficient ways to achieve the same goal in an automated fashion. Accordingly, for self-driving laboratories, we need to carefully rethink the translation of manual experimental protocols.
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Affiliation(s)
- Martin Seifrid
- Department
of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
| | - Robert Pollice
- Department
of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
| | | | - Zamyla Morgan Chan
- Department
of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
- Acceleration
Consortium, University of Toronto, Toronto, Ontario M5S 3H6, Canada
| | - Kazuhiro Hotta
- Department
of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
- Science
& Innovation Center, Mitsubishi Chemical
Corporation, 1000 Kamoshidacho, Aoba, Yokohama, Kanagawa 227-8502, Japan
| | - Cher Tian Ser
- Department
of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
| | - Jenya Vestfrid
- Department
of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
| | - Tony C. Wu
- Department
of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
| | - Alán Aspuru-Guzik
- Department
of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
- Department
of Computer Science, University of Toronto, Toronto, Ontario M5S 3H6, Canada
- Department
of Chemical Engineering & Applied Chemistry, University of Toronto, Toronto, Ontario M5S 3E5, Canada
- Department
of Materials Science, University of Toronto, Toronto, Ontario M5S 3E4, Canada
- Vector
Institute for Artificial Intelligence, Toronto, Ontario M5S 1M1, Canada
- Lebovic
Fellow, Canadian Institute for Advanced
Research, Toronto, Ontario M5S 1M1, Canada
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