Pub Date : 2025-07-23DOI: 10.1016/j.checat.2025.101458
Xingyu Wang, Yu Mao, Ziyun Wang
Searching for a transition state (TS) is crucial in understanding chemical reaction mechanisms and kinetics. While traditional computational methods, including single-ended and double-ended approaches, have provided valuable insights, they face significant computational cost and scalability limitations. This review comprehensively examines conventional computational approaches and the rapidly emerging machine learning (ML) methods for TS searching, highlighting the significant acceleration in ML method development since 2020. We first analyze traditional computational methods, discussing their theoretical foundations and practical limitations. We then systematically review available TS datasets that enable ML applications. The review explores the evolution of ML approaches from traditional methods like random forest and kernel ridge regression to advanced architectures such as graph neural networks, tensor field networks, and generative models. We examine current challenges, including data scarcity, computational constraints, and validation standards, while highlighting promising future directions. This comprehensive analysis provides insights into the field’s current state and outlines potential pathways for advancing TS searching methodologies.
{"title":"Machine learning approaches for transition state prediction","authors":"Xingyu Wang, Yu Mao, Ziyun Wang","doi":"10.1016/j.checat.2025.101458","DOIUrl":"https://doi.org/10.1016/j.checat.2025.101458","url":null,"abstract":"Searching for a transition state (TS) is crucial in understanding chemical reaction mechanisms and kinetics. While traditional computational methods, including single-ended and double-ended approaches, have provided valuable insights, they face significant computational cost and scalability limitations. This review comprehensively examines conventional computational approaches and the rapidly emerging machine learning (ML) methods for TS searching, highlighting the significant acceleration in ML method development since 2020. We first analyze traditional computational methods, discussing their theoretical foundations and practical limitations. We then systematically review available TS datasets that enable ML applications. The review explores the evolution of ML approaches from traditional methods like random forest and kernel ridge regression to advanced architectures such as graph neural networks, tensor field networks, and generative models. We examine current challenges, including data scarcity, computational constraints, and validation standards, while highlighting promising future directions. This comprehensive analysis provides insights into the field’s current state and outlines potential pathways for advancing TS searching methodologies.","PeriodicalId":53121,"journal":{"name":"Chem Catalysis","volume":"32 1","pages":""},"PeriodicalIF":9.4,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144684919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-17DOI: 10.1016/j.checat.2025.101461
Cole W. Hullfish, Michele L. Sarazen
In a recent Science publication, Mlekodaj, van Bokhoven, and colleagues use an anomalous X-ray powder diffraction method to quantitatively determine distributions of aluminum at specific T-sites in MFI zeolite, which has implications for advancing both the understanding of site-dependent kinetic phenomena and zeolite synthesis with deliberate aluminum siting.
{"title":"Toward revealing T-site distributions and resultant catalytic implications in MFI zeolites","authors":"Cole W. Hullfish, Michele L. Sarazen","doi":"10.1016/j.checat.2025.101461","DOIUrl":"https://doi.org/10.1016/j.checat.2025.101461","url":null,"abstract":"In a recent <em>Science</em> publication, Mlekodaj, van Bokhoven, and colleagues use an anomalous X-ray powder diffraction method to quantitatively determine distributions of aluminum at specific T-sites in MFI zeolite, which has implications for advancing both the understanding of site-dependent kinetic phenomena and zeolite synthesis with deliberate aluminum siting.","PeriodicalId":53121,"journal":{"name":"Chem Catalysis","volume":"37 1","pages":""},"PeriodicalIF":9.4,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144645683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-17DOI: 10.1016/j.checat.2025.101439
Tao Li, Haohua Huo
The field of radical chemistry has long faced a fundamental limitation: the instantaneous racemization of free radicals. Reporting in the June 5 issue of Nature, Baran and co-workers have now achieved stereoretentive radical cross-coupling through a unique mechanistic design, opening new synthetic pathways for preparing enantioenriched compounds.
{"title":"Radicals retain their memory in cross-coupling","authors":"Tao Li, Haohua Huo","doi":"10.1016/j.checat.2025.101439","DOIUrl":"https://doi.org/10.1016/j.checat.2025.101439","url":null,"abstract":"The field of radical chemistry has long faced a fundamental limitation: the instantaneous racemization of free radicals. Reporting in the June 5 issue of <em>Nature</em>, Baran and co-workers have now achieved stereoretentive radical cross-coupling through a unique mechanistic design, opening new synthetic pathways for preparing enantioenriched compounds.","PeriodicalId":53121,"journal":{"name":"Chem Catalysis","volume":"96 1","pages":""},"PeriodicalIF":9.4,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144645622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-17DOI: 10.1016/j.checat.2025.101440
Linlin Liu, ChungHyuk Lee
In the May 28 issue of the Journal of the American Chemical Society, Xue et al. report a single-atom Mn-integrated RuO2 electrocatalyst that achieves an efficient oxygen evolution reaction across a broad pH range while maintaining remarkable stability over 1,000 h. This Mn-modified catalyst exhibits high stability and activity in both proton-exchange membrane and alkaline water electrolysis.
{"title":"Scalable single-atom catalyst for high-performing and durable water electrolyzers","authors":"Linlin Liu, ChungHyuk Lee","doi":"10.1016/j.checat.2025.101440","DOIUrl":"https://doi.org/10.1016/j.checat.2025.101440","url":null,"abstract":"In the May 28 issue of the <em>Journal of the American Chemical Society</em>, Xue et al. report a single-atom Mn-integrated RuO<sub>2</sub> electrocatalyst that achieves an efficient oxygen evolution reaction across a broad pH range while maintaining remarkable stability over 1,000 h. This Mn-modified catalyst exhibits high stability and activity in both proton-exchange membrane and alkaline water electrolysis.","PeriodicalId":53121,"journal":{"name":"Chem Catalysis","volume":"24 1","pages":""},"PeriodicalIF":9.4,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144645591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-17DOI: 10.1016/j.checat.2025.101444
Yazhou Zhou, Guangbo Chen
In the April 25 issue of Science, Yue et al. present an innovative MOF@POM hybrid catalyst, which they designed by grafting CoFe-MOFs onto nickel-bridged POMs. The resulting catalyst sets a new benchmark for efficient and durable water oxidation by exhibiting outstanding performance in an anion-exchange membrane water electrolyzer.
{"title":"MOF@POM hybrid sets a new benchmark for alkaline water oxidation","authors":"Yazhou Zhou, Guangbo Chen","doi":"10.1016/j.checat.2025.101444","DOIUrl":"https://doi.org/10.1016/j.checat.2025.101444","url":null,"abstract":"In the April 25 issue of <em>Science</em>, Yue et al. present an innovative MOF@POM hybrid catalyst, which they designed by grafting CoFe-MOFs onto nickel-bridged POMs. The resulting catalyst sets a new benchmark for efficient and durable water oxidation by exhibiting outstanding performance in an anion-exchange membrane water electrolyzer.","PeriodicalId":53121,"journal":{"name":"Chem Catalysis","volume":"80 1","pages":""},"PeriodicalIF":9.4,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144645623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-15DOI: 10.1016/j.checat.2025.101460
Kimberly A.W. Reid, Randy Sutio, Jack M. Ranani, Maksym Pavlenko, Brennah E. Slaney, Christophe Allais, Johnny W. Lee, Christopher Sandford
The sustainable synthesis of amide bonds under mild conditions is a key green chemistry target for the pharmaceutical process industry and is highlighted as one of the ten goals of the American Chemical Society’s Green Chemistry Institute Pharmaceutical Roundtable. Here, we report an organocatalyst that can achieve the synthesis of amides at room temperature. The catalyst includes both boronic acid and phosphine oxide functionalities, which operate in concert to facilitate substrate activation. Unlike that of other arylboronic acid catalysts, the monomeric mechanism proceeds via a redox-neutral phosphorus(V) cycle, where the adjacent boronic acid is key to room-temperature activity.
{"title":"A bifunctional boronic acid/phosphorus(V) organocatalyst for the direct room-temperature amidation of carboxylic acids","authors":"Kimberly A.W. Reid, Randy Sutio, Jack M. Ranani, Maksym Pavlenko, Brennah E. Slaney, Christophe Allais, Johnny W. Lee, Christopher Sandford","doi":"10.1016/j.checat.2025.101460","DOIUrl":"https://doi.org/10.1016/j.checat.2025.101460","url":null,"abstract":"The sustainable synthesis of amide bonds under mild conditions is a key green chemistry target for the pharmaceutical process industry and is highlighted as one of the ten goals of the American Chemical Society’s Green Chemistry Institute Pharmaceutical Roundtable. Here, we report an organocatalyst that can achieve the synthesis of amides at room temperature. The catalyst includes both boronic acid and phosphine oxide functionalities, which operate in concert to facilitate substrate activation. Unlike that of other arylboronic acid catalysts, the monomeric mechanism proceeds via a redox-neutral phosphorus(V) cycle, where the adjacent boronic acid is key to room-temperature activity.","PeriodicalId":53121,"journal":{"name":"Chem Catalysis","volume":"203 1","pages":""},"PeriodicalIF":9.4,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144630095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-15DOI: 10.1016/j.checat.2025.101459
Ali Kamali, Joshua M. Little, Song Luo, Amy Chen, Akash Warty, Antara Bhowmick, Jorge Moncada, Evan P. Jahrman, Brandon C. Vance, Jong K. Keum, Taylor J. Woehl, Po-Yen Chen, Dionisios G. Vlachos, Dongxia Liu
The hydrogenolysis of plastics is limited by active-site inaccessibility and inefficient mass transport of bulky polymer chains. To overcome these challenges, this work developed two-dimensional MXene-supported Ru (Ru@MXene) catalysts. Lyophilization of a solution containing dispersed MXene sheets and Ru precursors enabled the confinement of Ru species within the MXene interlayers, which act as pillars to expand the interlayer spacing. Building on this, a silica-pillared MXene-supported Ru (Ru@P-MXene) with even larger interlayer spacing exhibited a reaction rate of 914.9 gC5–C35 gRu−1 h−1 for the hydrogenolysis of low-density polyethylene (LDPE) into valuable liquid chemicals (e.g., C5–C35). A comparison of product yields between Ru@P-MXene and Ru@MXene suggests that elongated Ru particles confined within the MXene support expose their side facets for the reaction. This work demonstrates a new application of MXene in thermochemical catalysis, offering a solution to the challenges of active-site accessibility, mass transport, and reaction confinement in chemical plastic upcycling.
{"title":"Plastic-waste hydrogenolysis over two-dimensional MXene-supported ruthenium catalysts with tunable interlayer spacing","authors":"Ali Kamali, Joshua M. Little, Song Luo, Amy Chen, Akash Warty, Antara Bhowmick, Jorge Moncada, Evan P. Jahrman, Brandon C. Vance, Jong K. Keum, Taylor J. Woehl, Po-Yen Chen, Dionisios G. Vlachos, Dongxia Liu","doi":"10.1016/j.checat.2025.101459","DOIUrl":"https://doi.org/10.1016/j.checat.2025.101459","url":null,"abstract":"The hydrogenolysis of plastics is limited by active-site inaccessibility and inefficient mass transport of bulky polymer chains. To overcome these challenges, this work developed two-dimensional MXene-supported Ru (Ru@MXene) catalysts. Lyophilization of a solution containing dispersed MXene sheets and Ru precursors enabled the confinement of Ru species within the MXene interlayers, which act as pillars to expand the interlayer spacing. Building on this, a silica-pillared MXene-supported Ru (Ru@P-MXene) with even larger interlayer spacing exhibited a reaction rate of 914.9 g<sub>C5–C35</sub> g<sub>Ru</sub><sup>−1</sup> h<sup>−1</sup> for the hydrogenolysis of low-density polyethylene (LDPE) into valuable liquid chemicals (e.g., C<sub>5</sub>–C<sub>35</sub>). A comparison of product yields between Ru@P-MXene and Ru@MXene suggests that elongated Ru particles confined within the MXene support expose their side facets for the reaction. This work demonstrates a new application of MXene in thermochemical catalysis, offering a solution to the challenges of active-site accessibility, mass transport, and reaction confinement in chemical plastic upcycling.","PeriodicalId":53121,"journal":{"name":"Chem Catalysis","volume":"59 17 1","pages":""},"PeriodicalIF":9.4,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144630097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-10DOI: 10.1016/j.checat.2025.101445
Ashutosh Kumar, Jan Taubitz, Fabian Meyer, Nicolas Imstepf, Jiaming Peng, Erika Tassano, Charles Moore, Thomas Lochmann, Radka Snajdrova, Rebecca Buller
Here, we report the development of EnzyMS, a Python-based pipeline for the analysis of high-resolution liquid chromatography-mass spectrometry (LC-MS) data specifically tailored for biocatalysis experiments. Applying EnzyMS to biocatalytic reactions carried out with variants of Fe(II)/α-ketoglutarate-dependent halogenase WelO5∗ on the antifungal macrolide soraphen A, we discovered reaction outcomes that had not been observable when using standard analysis software. Interestingly, we detected a previously unreported selective oxidative demethylation of soraphen A alongside the reported hydroxylations and chlorinations. Building on this finding, a computationally guided protein engineering approach allowed us to identify a WelO5∗ variant that exhibited a 3-fold improved demethylation performance by only creating and testing three predicted variants. In summary, we showcase the utility of the EnzyMS workflow and its potential to enable rapid detection of previously unobserved biocatalytic products and highlight the valuable synergies between data science pipelines and the computational design of enzymes.
{"title":"Streamlining enzyme discovery and development through data analysis and computation","authors":"Ashutosh Kumar, Jan Taubitz, Fabian Meyer, Nicolas Imstepf, Jiaming Peng, Erika Tassano, Charles Moore, Thomas Lochmann, Radka Snajdrova, Rebecca Buller","doi":"10.1016/j.checat.2025.101445","DOIUrl":"https://doi.org/10.1016/j.checat.2025.101445","url":null,"abstract":"Here, we report the development of EnzyMS, a Python-based pipeline for the analysis of high-resolution liquid chromatography-mass spectrometry (LC-MS) data specifically tailored for biocatalysis experiments. Applying EnzyMS to biocatalytic reactions carried out with variants of Fe(II)/α-ketoglutarate-dependent halogenase WelO5∗ on the antifungal macrolide soraphen A, we discovered reaction outcomes that had not been observable when using standard analysis software. Interestingly, we detected a previously unreported selective oxidative demethylation of soraphen A alongside the reported hydroxylations and chlorinations. Building on this finding, a computationally guided protein engineering approach allowed us to identify a WelO5∗ variant that exhibited a 3-fold improved demethylation performance by only creating and testing three predicted variants. In summary, we showcase the utility of the EnzyMS workflow and its potential to enable rapid detection of previously unobserved biocatalytic products and highlight the valuable synergies between data science pipelines and the computational design of enzymes.","PeriodicalId":53121,"journal":{"name":"Chem Catalysis","volume":"15 1","pages":""},"PeriodicalIF":9.4,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144594853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-10DOI: 10.1016/j.checat.2025.101431
Dean J. Tantillo
The importance of non-statistical dynamic effects on reactivity and selectivity for reactions catalyzed by homogeneous transition-metal-containing catalysts is highlighted. Fundamental principles of non-statistical behavior are laid out, examples from the literature are given to illustrate these principles, and guidelines for when to raise the alarm that such effects may be intervening in transition-metal-promoted reactions are provided.
{"title":"Dynamically controlled kinetic selectivity in reactions promoted by transition metal catalysts","authors":"Dean J. Tantillo","doi":"10.1016/j.checat.2025.101431","DOIUrl":"https://doi.org/10.1016/j.checat.2025.101431","url":null,"abstract":"The importance of non-statistical dynamic effects on reactivity and selectivity for reactions catalyzed by homogeneous transition-metal-containing catalysts is highlighted. Fundamental principles of non-statistical behavior are laid out, examples from the literature are given to illustrate these principles, and guidelines for when to raise the alarm that such effects may be intervening in transition-metal-promoted reactions are provided.","PeriodicalId":53121,"journal":{"name":"Chem Catalysis","volume":"21 1","pages":""},"PeriodicalIF":9.4,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144594852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-08DOI: 10.1016/j.checat.2025.101442
Yu-Fei Ao
Biocatalysis is a promising approach to asymmetric synthesis; however, the natural substrate specificity of enzymes often limits their stereoselectivity, and thus, protein engineering is essential to improving enzyme performance. This perspective summarizes machine learning-assisted protein engineering for stereoselectivity, focusing on supervised learning models trained on experimental data to uncover correlations between enzyme/substrate descriptors and stereoselectivity. This approach can provide relatively accurate predictions at low computational cost, thereby improving or reversing enzyme stereoselectivity. Despite these advances, challenges remain, such as the lack of reliable stereoselectivity data and limited predictive performance and generalization ability of models. The integration of large amounts of high-quality data, more accurate structural and physicochemical descriptors, and innovative algorithms holds the promise of developing more robust and generalizable models that can predict the stereoselectivity of a wide range of enzymes and substrates. This approach could pave the way for more efficient and sustainable biocatalytic processes in asymmetric synthesis.
{"title":"Machine learning-assisted protein engineering for improving stereoselectivity","authors":"Yu-Fei Ao","doi":"10.1016/j.checat.2025.101442","DOIUrl":"https://doi.org/10.1016/j.checat.2025.101442","url":null,"abstract":"Biocatalysis is a promising approach to asymmetric synthesis; however, the natural substrate specificity of enzymes often limits their stereoselectivity, and thus, protein engineering is essential to improving enzyme performance. This perspective summarizes machine learning-assisted protein engineering for stereoselectivity, focusing on supervised learning models trained on experimental data to uncover correlations between enzyme/substrate descriptors and stereoselectivity. This approach can provide relatively accurate predictions at low computational cost, thereby improving or reversing enzyme stereoselectivity. Despite these advances, challenges remain, such as the lack of reliable stereoselectivity data and limited predictive performance and generalization ability of models. The integration of large amounts of high-quality data, more accurate structural and physicochemical descriptors, and innovative algorithms holds the promise of developing more robust and generalizable models that can predict the stereoselectivity of a wide range of enzymes and substrates. This approach could pave the way for more efficient and sustainable biocatalytic processes in asymmetric synthesis.","PeriodicalId":53121,"journal":{"name":"Chem Catalysis","volume":"51 1","pages":""},"PeriodicalIF":9.4,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144578172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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