The challenge of balancing model sensitivity and robustness in predicting yields: a benchmarking study of amide coupling reactions

Zhen Liu, Yurii S. Moroz and Olexandr Isayev

Chem. Sci., 2023, 14, 10835
DOI: 10.1039/D3SC03902A

Synthesis planning in reaction space: a study on success, robustness and diversity

Alan Kai Hassen, Helen Lai, Samuel Genheden, Mike Preuss and Djork-Arné Clevert

Digital Discovery, 2026
DOI: 10.1039/D5DD00280J

Commit: Reaction classification and yield prediction using the differential reaction fingerprint DRFP

Daniel Probst

Digital Discovery, 2025, 4, 1988
DOI: 10.1039/D5DD00089K

On the use of real-world datasets for reaction yield prediction

Mandana Saebi, Bozhao Nan, John E. Herr, Jessica Wahlers, Zhichun Guo, Andrzej M. Zurański, Thierry Kogej, Per-Ola Norrby, Abigail G. Doyle, Nitesh V. Chawla and Olaf Wiest

Chem. Sci., 2023, 14, 4997
DOI: 10.1039/D2SC06041H

SynCat: molecule-level attention graph neural network for precise reaction classification

Phuoc-Chung Van Nguyen, Van-Thinh To, Ngoc-Vi Nguyen Tran, Tieu-Long Phan, Tuyen Ngoc Truong, Thomas Gärtner, Daniel Merkle and Peter F. Stadler

Digital Discovery, 2026, 5, 241
DOI: 10.1039/D5DD00367A

Regio-MPNN: predicting regioselectivity for general metal-catalyzed cross-coupling reactions using a chemical knowledge informed message passing neural network

Baochen Li, Yuru Liu, Haibin Sun, Rentao Zhang, Yongli Xie, Klement Foo, Frankie S. Mak, Ruimao Zhang, Tianshu Yu, Sen Lin, Peng Wang and Xiaoxue Wang

Digital Discovery, 2024, 3, 2019
DOI: 10.1039/D4DD00244J

Multistep retrosynthesis combining a disconnection aware triple transformer loop with a route penalty score guided tree search

David Kreutter and Jean-Louis Reymond

Chem. Sci., 2023, 14, 9959
DOI: 10.1039/D3SC01604H

The physics-AI dialogue in drug design

Pablo Andrés Vargas-Rosales and Amedeo Caflisch

RSC Med. Chem., 2025, 16, 1499
DOI: 10.1039/D4MD00869C

AI-driven hypergraph network of organic chemistry: network statistics and applications in reaction classification

Vipul Mann and Venkat Venkatasubramanian

React. Chem. Eng., 2023, 8, 619
DOI: 10.1039/D2RE00309K

Repurposing quantum chemical descriptor datasets for on-the-fly generation of informative reaction representations: application to hydrogen atom transfer reactions

Javier E. Alfonso-Ramos, Rebecca M. Neeser and Thijs Stuyver

Digital Discovery, 2024, 3, 919
DOI: 10.1039/D4DD00043A

Recent developments in the total synthesis of natural products using the Ugi multicomponent reactions as the key strategy

Enrique L. Larghi, Andrea B. J. Bracca, Sebastián O. Simonetti and Teodoro S. Kaufman

Org. Biomol. Chem., 2024, 22, 429
DOI: 10.1039/D3OB01837G

Alchemical analysis of FDA approved drugs

Markus Orsi, Daniel Probst, Philippe Schwaller and Jean-Louis Reymond

Digital Discovery, 2023, 2, 1289
DOI: 10.1039/D3DD00039G

Generating a skeleton reaction network for reactions of large-scale ReaxFF MD pyrolysis simulations based on a machine learning predicted reaction class

Shanwen Yang, Xiaoxia Li, Mo Zheng, Chunxing Ren and Li Guo

Phys. Chem. Chem. Phys., 2024, 26, 5649
DOI: 10.1039/D3CP05935A

Discrete and mixed-variable experimental design with surrogate-based approach

Mengjia Zhu, Austin Mroz, Lingfeng Gui, Kim E. Jelfs, Alberto Bemporad, Ehecatl Antonio del Río Chanona and Ye Seol Lee

Digital Discovery, 2024, 3, 2589
DOI: 10.1039/D4DD00113C

Benchmarking machine-readable vectors of chemical reactions on computed activation barriers

Puck van Gerwen, Ksenia R. Briling, Yannick Calvino Alonso, Malte Franke and Clemence Corminboeuf

Digital Discovery, 2024, 3, 932
DOI: 10.1039/D3DD00175J

Data augmentation in a triple transformer loop retrosynthesis model

Yves Grandjean, David Kreutter and Jean-Louis Reymond

Digital Discovery, 2026, 5, 653
DOI: 10.1039/D5DD00465A

Bayesian optimisation for additive screening and yield improvements – beyond one-hot encoding

Bojana Ranković, Ryan-Rhys Griffiths, Henry B. Moss and Philippe Schwaller

Digital Discovery, 2024, 3, 654
DOI: 10.1039/D3DD00096F

Investigating the reliability and interpretability of machine learning frameworks for chemical retrosynthesis

Friedrich Hastedt, Rowan M. Bailey, Klaus Hellgardt, Sophia N. Yaliraki, Ehecatl Antonio del Rio Chanona and Dongda Zhang

Digital Discovery, 2024, 3, 1194
DOI: 10.1039/D4DD00007B

Designing Buchwald–Hartwig Reaction Graph for Yield Prediction

Weiren Zhao, Shen Wang and Yang Li

J. Org. Chem., 2025, 90, 12975
DOI: 10.1021/acs.joc.5c01400

Global reactivity models are impactful in industrial synthesis applications

Paulo Neves, Kelly McClure, Jonas Verhoeven, Natalia Dyubankova, Ramil Nugmanov, Andrey Gedich, Sairam Menon, Zhicai Shi and Jörg K. Wegner

J Cheminform, 2023, 15
DOI: 10.1186/s13321-023-00685-0

Survey on recent progress of AI for chemistry: methods, applications, and opportunities

Hu Ding, Pengxiang Hua and Zhen Huang

Front. Comput. Sci., 2026, 20
DOI: 10.1007/s11704-025-50127-3

A general model for predicting enzyme functions based on enzymatic reactions

Wenjia Qian, Xiaorui Wang, Yu Kang, Peichen Pan, Tingjun Hou and Chang-Yu Hsieh

J Cheminform, 2024, 16
DOI: 10.1186/s13321-024-00827-y

EnzChemRED, a rich enzyme chemistry relation extraction dataset

Po-Ting Lai, Elisabeth Coudert, Lucila Aimo, Kristian Axelsen, Lionel Breuza, Edouard de Castro, Marc Feuermann, Anne Morgat, Lucille Pourcel, Ivo Pedruzzi, Sylvain Poux, Nicole Redaschi, Catherine Rivoire, Anastasia Sveshnikova, Chih-Hsuan Wei, Robert Leaman, Ling Luo, Zhiyong Lu and Alan Bridge

Sci Data, 2024, 11
DOI: 10.1038/s41597-024-03835-7

Deep Kernel learning for reaction outcome prediction and optimization

Sukriti Singh and José Miguel Hernández-Lobato

Commun Chem, 2024, 7
DOI: 10.1038/s42004-024-01219-x

AI-driven parametrization of Michaelis–Menten maximal velocity: Advancing in silico new approach methodologies (NAMs)

Achilleas Karakoltzidis, Spyros P. Karakitsios and Dimosthenis Α. Sarigiannis

NAM Journal, 2025, 1, 100012
DOI: 10.1016/j.namjnl.2025.100012

Bridging the gap between chemical reaction pretraining and conditional molecule generation with a unified model

Bo Qiang, Yiran Zhou, Yuheng Ding, Ningfeng Liu, Song Song, Liangren Zhang, Bo Huang and Zhenming Liu

Nat Mach Intell, 2023, 5, 1476
DOI: 10.1038/s42256-023-00764-9

3DReact: Geometric Deep Learning for Chemical Reactions

Puck van Gerwen, Ksenia R. Briling, Charlotte Bunne, Vignesh Ram Somnath, Ruben Laplaza, Andreas Krause and Clemence Corminboeuf

J. Chem. Inf. Model., 2024, 64, 5771
DOI: 10.1021/acs.jcim.4c00104

Molecular Machine Learning for Chemical Catalysis: Prospects and Challenges

Sukriti Singh and Raghavan B. Sunoj

Acc. Chem. Res., 2023, 56, 402
DOI: 10.1021/acs.accounts.2c00801

Prediction of chemical reaction yields with large-scale multi-view pre-training

Runhan Shi, Gufeng Yu, Xiaohong Huo and Yang Yang

J Cheminform, 2024, 16
DOI: 10.1186/s13321-024-00815-2

General Chemically Intuitive Atom- and Bond-Level DFT Descriptors for Machine Learning Approaches to Reaction Condition Prediction

Miguel Nouman, Richard B. Canty, Brent A. Koscher, Matthew A. McDonald and Klavs F. Jensen

J. Chem. Inf. Model., 2025, 65, 6499
DOI: 10.1021/acs.jcim.4c02255

HiRXN: Hierarchical Attention-Based Representation Learning for Chemical Reaction

Yahui Cao, Tao Zhang, Xin Zhao and Haotong Li

J. Chem. Inf. Model., 2025, 65, 1990
DOI: 10.1021/acs.jcim.4c01787

ChemOrigins: a community-curated database for origins-of-life chemistry

Bruno Cuevas-Zuviria, Zach Adam, Daniel Cove and Betül Kaçar

Interface Focus, 2025, 15
DOI: 10.1098/rsfs.2025.0034

Using Active Learning to Develop Machine Learning Models for Reaction Yield Prediction

Simon Viet Johansson, Hampus Gummesson Svensson, Esben Bjerrum, Alexander Schliep, Morteza Haghir Chehreghani, Christian Tyrchan and Ola Engkvist

Molecular Informatics, 2022, 41
DOI: 10.1002/minf.202200043

Recent Applications of Machine Learning in Molecular Property and Chemical Reaction Outcome Predictions

Shilpa Shilpa, Gargee Kashyap and Raghavan B. Sunoj

J. Phys. Chem. A, 2023, 127, 8253
DOI: 10.1021/acs.jpca.3c04779

LinChemIn: SynGraph—a data model and a toolkit to analyze and compare synthetic routes

Marta Pasquini and Marco Stenta

J Cheminform, 2023, 15
DOI: 10.1186/s13321-023-00714-y

The converging moment of chemistry and artificial intelligence: a review and outlook

Wei Zhang, Yidong Zhang, Jiacheng Xiong, Mingyue Zheng and Kaixian Chen

Sci. Sin.-Chim, 2025, 55, 2361
DOI: 10.1360/SSC-2025-0111

Discovery, design, and engineering of enzymes based on molecular retrobiosynthesis

Ancheng Chen, Xiangda Peng, Tao Shen, Liangzhen Zheng, Dong Wu and Sheng Wang

mLife, 2025, 4, 107
DOI: 10.1002/mlf2.70009

MetaRF: attention-based random forest for reaction yield prediction with a few trails

Kexin Chen, Guangyong Chen, Junyou Li, Yuansheng Huang, Ercheng Wang, Tingjun Hou and Pheng-Ann Heng

J Cheminform, 2023, 15
DOI: 10.1186/s13321-023-00715-x

Machine learning approach for predicting the yield of pyrroles and dipyrromethanes condensation reactions with aldehydes

Dmitry M. Makarov, Michail M. Lukanov, Aleksey I. Rusanov, Nugzar Zh. Mamardashvili and Alexander A. Ksenofontov

Journal of Computational Science, 2023, 74, 102173
DOI: 10.1016/j.jocs.2023.102173

Physics-based representations for machine learning properties of chemical reactions

Puck van Gerwen, Alberto Fabrizio, Matthew D Wodrich and Clemence Corminboeuf

Mach. Learn.: Sci. Technol., 2022, 3, 045005
DOI: 10.1088/2632-2153/ac8f1a

Dual-encoder contrastive learning accelerates enzyme discovery

Jason W. Rocks, Dat P. Truong, Dmitrij Rappoport, Samuel Maddrell-Mander, Daniel A. Martin-Alarcon, Toni M. Lee, Steven Crossan and Joshua E. Goldford

Proc. Natl. Acad. Sci. U.S.A., 2026, 123
DOI: 10.1073/pnas.2520070123

Probing the origin of higher efficiency of terphenyl phosphine over the biaryl framework in Pd-catalyzed C-N coupling: A combined DFT and machine learning study

Qingfu Ye, Yu Zhao and Jun Zhu

Artificial Intelligence Chemistry, 2023, 1, 100005
DOI: 10.1016/j.aichem.2023.100005

Multi‐modal Homogeneous Chemical Reaction Performance Prediction with Graph and Chemical Language Information

Shen Wang, Weiren Zhao, Yining Liu and Yang Li

Chin. J. Chem., 2025, 43, 1230
DOI: 10.1002/cjoc.202401186

Reaction rebalancing: a novel approach to curating reaction databases

Tieu-Long Phan, Klaus Weinbauer, Thomas Gärtner, Daniel Merkle, Jakob L. Andersen, Rolf Fagerberg and Peter F. Stadler

J Cheminform, 2024, 16
DOI: 10.1186/s13321-024-00875-4

Metal-catalyzed homogeneous catalytic transfer hydrogenation: A data-driven approach

Alfredo Pereira and Oleksandra S. Trofymchuk

Molecular Catalysis, 2026, 588, 115563
DOI: 10.1016/j.mcat.2025.115563

Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR

Alexander Tropsha, Olexandr Isayev, Alexandre Varnek, Gisbert Schneider and Artem Cherkasov

Nat Rev Drug Discov, 2024, 23, 141
DOI: 10.1038/s41573-023-00832-0

Exploring Chemical Reaction Space with Machine Learning Models: Representation and Feature Perspective

Yuheng Ding, Bo Qiang, Qixuan Chen, Yiqiao Liu, Liangren Zhang and Zhenming Liu

J. Chem. Inf. Model., 2024, 64, 2955
DOI: 10.1021/acs.jcim.4c00004

A geometric foundation model for enzyme retrieval with evolutionary insights

Yong Liu, Chenqing Hua, Menglong Xu, Tao Zeng, Jiahua Rao, Zhongyue Zhang, Ruibo Wu, Jing-Ke Weng, Connor W. Coley and Shuangjia Zheng

Nat Catal, 2026, 9, 148
DOI: 10.1038/s41929-026-01478-y

Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning

Alexander Kroll, Yvan Rousset, Xiao-Pan Hu, Nina A. Liebrand and Martin J. Lercher

Nat Commun, 2023, 14
DOI: 10.1038/s41467-023-39840-4

Catalyst-Free C–N Coupling under Ambient Conditions via High-Throughput Microdroplet Reactions

Jie Li, Santeri Aikonen, Nicolás M. Morato, Bo Hao, Zhicai Shi, Iulia I. Strambeanu and R. Graham Cooks

J. Org. Chem., 2025, 90, 18172
DOI: 10.1021/acs.joc.5c02022

Molecular set representation learning

Maria Boulougouri, Pierre Vandergheynst and Daniel Probst

Nat Mach Intell, 2024, 6, 754
DOI: 10.1038/s42256-024-00856-0

ChemTorch: A Deep Learning Framework for Benchmarking and Developing Chemical Reaction Property Prediction Models

Jasper De Landsheere, Anton Zamyatin, Johannes Karwounopoulos and Esther Heid

J. Chem. Inf. Model., 2026, 66, 2434
DOI: 10.1021/acs.jcim.5c02645

Enhancing chemical reaction search through contrastive representation learning and human-in-the-loop

Youngchun Kwon, Hyunjeong Jeon, Joonhyuk Choi, Youn-Suk Choi and Seokho Kang

J Cheminform, 2025, 17
DOI: 10.1186/s13321-025-00987-5

SynTemp: Efficient Extraction of Graph-Based Reaction Rules from Large-Scale Reaction Databases

Tieu-Long Phan, Klaus Weinbauer, Marcos E. González Laffitte, Yingjie Pan, Daniel Merkle, Jakob L. Andersen, Rolf Fagerberg, Christoph Flamm and Peter F. Stadler

J. Chem. Inf. Model., 2025, 65, 2882
DOI: 10.1021/acs.jcim.4c01795

Design and Experimental Validation of a Photocatalyst Recommender Based on a Large Language Model

Francis Millward, Michał Kulczykowski, Jay Badland‐Shaw, Sara Szymkuc, Rajan Suraksha, Aniket Kumar Srivastawa, Violaine Manet, Máire Griffin, Megan Bryden, Thomas Comerford, Lea Hämmerling, Aminata Mariko, Bartosz A. Grzybowski and Eli Zysman‐Colman

Angewandte Chemie, 2026, 138
DOI: 10.1002/ange.202514544

When Yield Prediction Does Not Yield Prediction: An Overview of the Current Challenges

Varvara Voinarovska, Mikhail Kabeshov, Dmytro Dudenko, Samuel Genheden and Igor V. Tetko

J. Chem. Inf. Model., 2024, 64, 42
DOI: 10.1021/acs.jcim.3c01524

YieldFCP: Enhancing Reaction Yield Prediction via Fine-grained Cross-modal Pre-training

Runhan Shi, Gufeng Yu, Letian Chen and Yang Yang

Artificial Intelligence Chemistry, 2025, 3, 100085
DOI: 10.1016/j.aichem.2025.100085

DeepMolecules: a web server for predicting enzyme and transporter–small molecule interactions

Alexander Kroll, Yvan Rousset, Thomas Spitzlei and Martin J Lercher

Nucleic Acids Research, 2025, 53, W213
DOI: 10.1093/nar/gkaf343

Leveraging large language models for predictive chemistry

Kevin Maik Jablonka, Philippe Schwaller, Andres Ortega-Guerrero and Berend Smit

Nat Mach Intell, 2024, 6, 161
DOI: 10.1038/s42256-023-00788-1

Machine Learning Algorithm Guides Catalyst Choices for Magnesium‐Catalyzed Asymmetric Reactions

Paulina Baczewska, Michał Kulczykowski, Bartosz Zambroń, Joanna Jaszczewska‐Adamczak, Zbigniew Pakulski, Rafał Roszak, Bartosz A. Grzybowski and Jacek Mlynarski

Angewandte Chemie, 2024, 136
DOI: 10.1002/ange.202318487

Enzyme association for environmental biotransformation reactions through contrastive learning of reaction center-specific fingerprints

Kunyang Zhang, Thierry D Marti, Silke I Probst, Serina L Robinson, Kathrin Fenner and Daisuke Kihara

Bioinformatics, 2026, 42
DOI: 10.1093/bioinformatics/btag142

Efficient Machine Learning for Yield Prediction in Transition Metal-Catalyzed meta-C(sp2)–H Bond Activation and Other Reactions

Supratim Ghosh, Nupur Jain and Raghavan B. Sunoj

ACS Catal., 2025, 15, 20251
DOI: 10.1021/acscatal.5c07050

HiCLR: Knowledge-Induced Hierarchical Contrastive Learning with Retrosynthesis Prediction Yields a Reaction Foundation Model

Jialu Wu, Yiheng Zhu, Xiaorui Wang, Yitong Li, Mingze Yin, Tianyue Wang, Yuqiang Han, Yu Kang, Yafeng Deng, Jian Wu, Chang-Yu Hsieh and Tingjun Hou

JACS Au, 2025, 5, 3140
DOI: 10.1021/jacsau.5c00289

REME: an integrated platform for reaction enzyme mining and evaluation

Zhenkun Shi, Dehang Wang, Yang Li, Rui Deng, Jiawei Lin, Cui Liu, Haoran Li, Ruoyu Wang, Muqiang Zhao, Zhitao Mao, Qianqian Yuan, Xiaoping Liao and Hongwu Ma

Nucleic Acids Research, 2024, 52, W299
DOI: 10.1093/nar/gkae405

An Ensemble Structure and Physicochemical (SPOC) Descriptor for Machine‐Learning Prediction of Chemical Reaction and Molecular Properties

Qi Yang, Yidi Liu, Junjie Cheng, Yao Li, Siyuan Liu, Yingdong Duan, Long Zhang and Sanzhong Luo

ChemPhysChem, 2022, 23
DOI: 10.1002/cphc.202200255

Self-Supervised Contrastive Molecular Representation Learning with a Chemical Synthesis Knowledge Graph

Jiancong Xie, Yi Wang, Jiahua Rao, Shuangjia Zheng and Yuedong Yang

J. Chem. Inf. Model., 2024, 64, 1945
DOI: 10.1021/acs.jcim.4c00157

Towards global reaction feasibility and robustness prediction with high throughput data and bayesian deep learning

Haowen Zhong, Yilan Liu, Haibin Sun, Yuru Liu, Rentao Zhang, Baochen Li, Yi Yang, Yuqing Huang, Fei Yang, Frankie S. Mak, Klement Foo, Sen Lin, Tianshu Yu, Peng Wang and Xiaoxue Wang

Nat Commun, 2025, 16
DOI: 10.1038/s41467-025-59812-0

Enhancing Generic Reaction Yield Prediction through Reaction Condition-Based Contrastive Learning

Xiaodan Yin, Chang-Yu Hsieh, Xiaorui Wang, Zhenxing Wu, Qing Ye, Honglei Bao, Yafeng Deng, Hongming Chen, Pei Luo, Huanxiang Liu, Tingjun Hou and Xiaojun Yao

Research, 2024, 7
DOI: 10.34133/research.0292

Learning Hierarchical Representations for Explainable Chemical Reaction Prediction

Jingyi Hou and Zhen Dong

Applied Sciences, 2023, 13, 5311
DOI: 10.3390/app13095311

Automatic Orbital Pair Selection for Multilevel Local Coupled-Cluster Based on Orbital Maps

Lukas Lampe and Johannes Neugebauer

J. Chem. Theory Comput., 2024, 20, 9407
DOI: 10.1021/acs.jctc.4c00885

Machine learning-guided strategies for reaction conditions design and optimization

Lung-Yi Chen and Yi-Pei Li

Beilstein J. Org. Chem., 2024, 20, 2476
DOI: 10.3762/bjoc.20.212

Comment on ‘Physics-based representations for machine learning properties of chemical reactions’

Kevin A Spiekermann, Thijs Stuyver, Lagnajit Pattanaik and William H Green

Mach. Learn.: Sci. Technol., 2023, 4, 048001
DOI: 10.1088/2632-2153/acee42

log-RRIM: Yield Prediction via Local-to-Global Reaction Representation Learning and Interaction Modeling

Xiao Hu, Ziqi Chen, Daniel Adu-Ampratwum and Xia Ning

J. Chem. Inf. Model., 2025, 65, 8042
DOI: 10.1021/acs.jcim.5c01208

Reaction performance prediction with an extrapolative and interpretable graph model based on chemical knowledge

Shu-Wen Li, Li-Cheng Xu, Cheng Zhang, Shuo-Qing Zhang and Xin Hong

Nat Commun, 2023, 14
DOI: 10.1038/s41467-023-39283-x

POxload: Machine Learning Estimates Drug Loadings of Polymeric Micelles

Josef Kehrein, Alex Bunker and Robert Luxenhofer

Mol. Pharmaceutics, 2024, 21, 3356
DOI: 10.1021/acs.molpharmaceut.4c00086

Graph Neural Networks in Modern AI-Aided Drug Discovery

Odin Zhang, Haitao Lin, Xujun Zhang, Xiaorui Wang, Zhenxing Wu, Qing Ye, Weibo Zhao, Jike Wang, Kejun Ying, Yu Kang, Chang-Yu Hsieh and Tingjun Hou

Chem. Rev., 2025, 125, 10001
DOI: 10.1021/acs.chemrev.5c00461

Completion of partial chemical equations

Federico Zipoli, Zeineb Ayadi, Philippe Schwaller, Teodoro Laino and Alain C Vaucher

Mach. Learn.: Sci. Technol., 2024, 5, 025071
DOI: 10.1088/2632-2153/ad5413

SynCluster: Reaction Type Clustering and Recommendation Framework for Synthesis Planning

Tiantao Liu, Zheng Cao, Yuansheng Huang, Yue Wan, Jian Wu, Chang-Yu Hsieh, Tingjun Hou and Yu Kang

JACS Au, 2023, 3, 3446
DOI: 10.1021/jacsau.3c00607

Recent Advancements in the Application of Artificial Intelligence in Drug Molecular Generation and Synthesis Planning

Buyong Ma, Yiguo Wang, Xingzi Li, Chang Shen, Hao Lin, Chenxi Du, Shanlin Yang, Ruoqing Zeng, Xuyang Tang, Jinglei Hu, Yukun Yang, Jingwen Wang, Jiawei Zhu, Xingqian Shan, Yu Zhang and Jiaqing Hu

Pharmaceutical Fronts, 2024, 06, e394
DOI: 10.1055/s-0044-1796647

Improving the Reliability of, and Confidence in, DFT Functional Benchmarking through Active Learning

Javier E. Alfonso-Ramos, Carlo Adamo, Éric Brémond and Thijs Stuyver

J. Chem. Theory Comput., 2025, 21, 1752
DOI: 10.1021/acs.jctc.4c01729

A Machine Learning Approach to Model Interaction Effects: Development and Application to Alcohol Deoxyfluorination

Andrzej M. Żurański, Shivaani S. Gandhi and Abigail G. Doyle

J. Am. Chem. Soc., 2023, 145, 7898
DOI: 10.1021/jacs.2c13093

Bridging chemistry and artificial intelligence by a reaction description language

Jiacheng Xiong, Wei Zhang, Yinquan Wang, Jiatao Huang, Yuqi Shi, Mingyan Xu, Manjia Li, Zunyun Fu, Xiangtai Kong, Yitian Wang, Zhaoping Xiong and Mingyue Zheng

Nat Mach Intell, 2025, 7, 782
DOI: 10.1038/s42256-025-01032-8

An open-source family of large encoder-decoder foundation models for chemistry

Eduardo Soares, Emilio Vital Brazil, Victor Shirasuna, Dmitry Zubarev, Renato Cerqueira and Kristin Schmidt

Commun Chem, 2025, 8
DOI: 10.1038/s42004-025-01585-0

Interpretable Machine Learning Enabled Inorganic Reaction Classification and Synthesis Condition Prediction

Christopher Karpovich, Elton Pan, Zach Jensen and Elsa Olivetti

Chem. Mater., 2023, 35, 1062
DOI: 10.1021/acs.chemmater.2c03010

An active representation learning method for reaction yield prediction with small-scale data

Peng-Xiang Hua, Zhen Huang, Zhe-Yuan Xu, Qiang Zhao, Chen-Yang Ye, Yi-Feng Wang, Yun-He Xu, Yao Fu and Hu Ding

Commun Chem, 2025, 8
DOI: 10.1038/s42004-025-01434-0

A Mamba-based foundation model for materials

Eduardo Soares, Emilio Vital Brazil, Victor Shirasuna, Dmitry Zubarev, Renato Cerqueira and Kristin Schmidt

npj Artif. Intell., 2025, 1
DOI: 10.1038/s44387-025-00009-7

Enzyformer: a two-stage pretrained model for enzymatic retrosynthesis

Tiantao Liu, Jiangcheng Xu, Xinke Zhan, Shaolong Lin and Shirley W. I. Siu

J Cheminform, 2026, 18
DOI: 10.1186/s13321-026-01164-y

Leveraging Language Model Multitasking To Predict C–H Borylation Selectivity

Ruslan Kotlyarov, Konstantinos Papachristos, Geoffrey P. F. Wood and Jonathan M. Goodman

J. Chem. Inf. Model., 2024, 64, 4286
DOI: 10.1021/acs.jcim.4c00137

AutoTemplate: enhancing chemical reaction datasets for machine learning applications in organic chemistry

Lung-Yi Chen and Yi-Pei Li

J Cheminform, 2024, 16
DOI: 10.1186/s13321-024-00869-2

ORDerly: Data Sets and Benchmarks for Chemical Reaction Data

Daniel S. Wigh, Joe Arrowsmith, Alexander Pomberger, Kobi C. Felton and Alexei A. Lapkin

J. Chem. Inf. Model., 2024, 64, 3790
DOI: 10.1021/acs.jcim.4c00292

Machine Learning Accelerated Analysis of Chemical Reaction Networks for Gas-Phase Reaction Systems

Yan Liu, Yiming Mo and Youwei Cheng

Ind. Eng. Chem. Res., 2025, 64, 5200
DOI: 10.1021/acs.iecr.4c03938

A deep learning framework for accurate reaction prediction and its application on high-throughput experimentation data

Baiqing Li, Shimin Su, Chan Zhu, Jie Lin, Xinyue Hu, Lebin Su, Zhunzhun Yu, Kuangbiao Liao and Hongming Chen

J Cheminform, 2023, 15
DOI: 10.1186/s13321-023-00732-w

CLAIRE: a contrastive learning-based predictor for EC number of chemical reactions

Zishuo Zeng, Jin Guo, Jiao Jin and Xiaozhou Luo

J Cheminform, 2025, 17
DOI: 10.1186/s13321-024-00944-8

Mechanism-Aware Deep Learning for Polar Reaction Prediction

Ryan J. Miller, Alexander E. Dashuta, Brayden Rudisill, David Van Vranken and Pierre Baldi

J. Am. Chem. Soc., 2025, 147, 41168
DOI: 10.1021/jacs.5c16838

SymChemAI: A Symbolically Informed Neural Network for Physicochemical Modeling of Reaction Systems

Souad Tayane, Jaafar Gaber, B. Benhala, M. El Khaili, H. Nhaila, A. Raihani, M. Qbadou and A. Sallem

E3S Web Conf., 2025, 680, 00109
DOI: 10.1051/e3sconf/202568000109

Exploring BERT for Reaction Yield Prediction: Evaluating the Impact of Tokenization, Molecular Representation, and Pretraining Data Augmentation

Adrian Krzyzanowski, Stephen D. Pickett and Peter Pogány

J. Chem. Inf. Model., 2025, 65, 4381
DOI: 10.1021/acs.jcim.5c00359

MITE: the Minimum Information about a Tailoring Enzyme database for capturing specialized metabolite biosynthesis

Adriano Rutz, Daniel Probst, César Aguilar, Daniel Y Akiyama, Fabrizio Alberti, Hannah E Augustijn, Nicole E Avalon, Christine Beemelmanns, Hellen Bertoletti Barbieri, Friederike Biermann, Alan J Bridge, Esteban Charria Girón, Russell Cox, Max Crüsemann, Paul M D’Agostino, Marc Feuermann, Jennifer Gerke, Karina Gutiérrez García, Jonathan E Holme, Ji-Yeon Hwang, Riccardo Iacovelli, Júlio César Jeronimo Barbosa, Navneet Kaur, Martin Klapper, Anna M Köhler, Aleksandra Korenskaia, Noel Kubach, Byung T Lee, Catarina Loureiro, Shrikant Mantri, Simran Narula, David Meijer, Jorge C Navarro-Muñoz, Giang-Son Nguyen, Sunaina Paliyal, Mohit Panghal, Latika Rao, Simon Sieber, Nika Sokolova, Sven T Sowa, Judit Szenei, Barbara R Terlouw, Heiner G Weddeling, Jingwei Yu, Nadine Ziemert, Tilmann Weber, Kai Blin, Justin J J van der Hooft, Marnix H Medema and Mitja M Zdouc

Nucleic Acids Research, 2026, 54, D635
DOI: 10.1093/nar/gkaf969

An explainability framework for deep learning on chemical reactions exemplified by enzyme-catalysed reaction classification

Daniel Probst

J Cheminform, 2023, 15
DOI: 10.1186/s13321-023-00784-y

Challenges with Literature-Derived Data in Machine Learning for Yield Prediction: A Case Study on Pd-Catalyzed Carbonylation Reactions

Dong-Zhi Li and Xue-Qing Gong

J. Phys. Chem. A, 2024, 128, 10423
DOI: 10.1021/acs.jpca.4c05489

Design and Experimental Validation of a Photocatalyst Recommender Based on a Large Language Model

Francis Millward, Michał Kulczykowski, Jay Badland‐Shaw, Sara Szymkuc, Rajan Suraksha, Aniket Kumar Srivastawa, Violaine Manet, Máire Griffin, Megan Bryden, Thomas Comerford, Lea Hämmerling, Aminata Mariko, Bartosz A. Grzybowski and Eli Zysman‐Colman

Angew Chem Int Ed, 2026, 65
DOI: 10.1002/anie.202514544

A virtual platform for automated hybrid organic-enzymatic synthesis planning

Xiaorui Wang, Xiaodan Yin, Xujun Zhang, Huifeng Zhao, Shukai Gu, Zhenxing Wu, Odin Zhang, Wenjia Qian, Yuansheng Huang, Yuquan Li, Dejun Jiang, Mingyang Wang, Huanxiang Liu, Xiaojun Yao, Chang-Yu Hsieh and Tingjun Hou

Nat Commun, 2025, 16
DOI: 10.1038/s41467-025-65898-3

Language Models for Predicting Organic Synthesis Procedures

Mantas Vaškevičius and Jurgita Kapočiūtė-Dzikienė

Applied Sciences, 2024, 14, 11526
DOI: 10.3390/app142411526

Optimizing Model Learning Performance on a Challenging Heck Reaction Yield Data Set

Shen Wang, Yining Liu, Weiren Zhao and Yang Li

J. Org. Chem., 2025, 90, 12768
DOI: 10.1021/acs.joc.5c01641

Applying Active Learning toward Building a Generalizable Model for Ni-Photoredox Cross-Electrophile Coupling of Aryl and Alkyl Bromides

Lucas W. Souza, Nathan D. Ricke, Braden C. Chaffin, Mike E. Fortunato, Shutian Jiang, Cihan Soylu, Thomas C. Caya, Sii Hong Lau, Katherine A. Wieser, Abigail G. Doyle and Kian L. Tan

J. Am. Chem. Soc., 2025, 147, 18747
DOI: 10.1021/jacs.5c02218

Machine Learning C–N Couplings: Obstacles for a General-Purpose Reaction Yield Prediction

Martin Fitzner, Georg Wuitschik, Raffael Koller, Jean-Michel Adam and Torsten Schindler

ACS Omega, 2023, 8, 3017
DOI: 10.1021/acsomega.2c05546

Open-Source Machine Learning in Computational Chemistry

Alexander Hagg and Karl N. Kirschner

J. Chem. Inf. Model., 2023, 63, 4505
DOI: 10.1021/acs.jcim.3c00643

High-throughput experimentation and machine learning-promoted synthesis of α-phosphoryloxy ketones via Ru-catalyzed P(O)O-H insertion reactions of sulfoxonium ylides

An Lin, Jingyuan Liu, Yougen Xu, Haiting Wu, Yating Chen, Yan Zhang, Lebin Su, Xiaodan Zhao and Kuangbiao Liao

Sci. China Chem., 2025, 68, 679
DOI: 10.1007/s11426-024-2313-5

Machine Learning Algorithm Guides Catalyst Choices for Magnesium‐Catalyzed Asymmetric Reactions

Paulina Baczewska, Michał Kulczykowski, Bartosz Zambroń, Joanna Jaszczewska‐Adamczak, Zbigniew Pakulski, Rafał Roszak, Bartosz A. Grzybowski and Jacek Mlynarski

Angew Chem Int Ed, 2024, 63
DOI: 10.1002/anie.202318487

RetroRanker: leveraging reaction changes to improve retrosynthesis prediction through re-ranking

Junren Li, Lei Fang and Jian-Guang Lou

J Cheminform, 2023, 15
DOI: 10.1186/s13321-023-00727-7

BatGPT-Chem: A Foundation Large Model for Chemical Engineering

Yifei Yang, Runhan Shi, Zuchao Li, Shu Jiang, Bao-Liang Lu, Qibin Zhao, Yang Yang and Hai Zhao

Research, 2025, 8
DOI: 10.34133/research.0827

Machine learning applications for thermochemical and kinetic property prediction

Lowie Tomme, Yannick Ureel, Maarten R. Dobbelaere, István Lengyel, Florence H. Vermeire, Christian V. Stevens and Kevin M. Van Geem

Reviews in Chemical Engineering, 2025, 41, 419
DOI: 10.1515/revce-2024-0027

Interpretation of chemical reaction yields with graph neural additive network

Youngchun Kwon, Yongsik Jung, Youn-Suk Choi and Seokho Kang

Mach. Learn.: Sci. Technol., 2025, 6, 025054
DOI: 10.1088/2632-2153/addfaa

Local reaction condition optimization via machine learning

Wenhuan Song and Honggang Sun

J Mol Model, 2025, 31
DOI: 10.1007/s00894-025-06365-0

Application of Transformers to Chemical Synthesis

Dong Jin, Yuli Liang, Zihao Xiong, Xiaojie Yang, Haifeng Wang, Jie Zeng and Shuangxi Gu

Molecules, 2025, 30, 493
DOI: 10.3390/molecules30030493

Machine Learning Yield Prediction from NiCOlit, a Small-Size Literature Data Set of Nickel Catalyzed C–O Couplings

Jules Schleinitz, Maxime Langevin, Yanis Smail, Benjamin Wehnert, Laurence Grimaud and Rodolphe Vuilleumier

J. Am. Chem. Soc., 2022, 144, 14722
DOI: 10.1021/jacs.2c05302

Enhancing kcat prediction through residue-aware attention mechanism and pre-trained representations

Yunxiang Cai, Fengya Ge, Chuanlei Zhang, Hao Chen, Xuan Qi, Xiaoping Liao and Lin Wang

Commun Biol, 2026, 9
DOI: 10.1038/s42003-026-09551-9

Transformer Performance for Chemical Reactions: Analysis of Different Predictive and Evaluation Scenarios

Fernando Jaume-Santero, Alban Bornet, Alain Valery, Nona Naderi, David Vicente Alvarez, Dimitrios Proios, Anthony Yazdani, Colin Bournez, Thomas Fessard and Douglas Teodoro

J. Chem. Inf. Model., 2023, 63, 1914
DOI: 10.1021/acs.jcim.2c01407

Developing Pharmaceutically Relevant Pd-Catalyzed C–N Coupling Reactivity Models Leveraging High-Throughput Experimentation

Seung Kyun Ha, Dipannita Kalyani, Michael S. West, Jessica Xu, Yu-hong Lam, Thomas Struble, Spencer Dreher, Shane W. Krska, Stephen L. Buchwald and Klavs F. Jensen

J. Am. Chem. Soc., 2025, 147, 19602
DOI: 10.1021/jacs.5c00933

Development of Reaction-Centered Encoders and Benchmarking of Enzyme-Reaction Pair Models

Stefan C. Pate, Eric H. Wang, Linda J. Broadbelt and Keith E. J. Tyo

J. Chem. Inf. Model., 2026, 66, 3496
DOI: 10.1021/acs.jcim.5c02755

Combining Molecular Quantum Mechanical Modeling and Machine Learning for Accelerated Reaction Screening and Discovery

Nicholas Casetti, Javier E. Alfonso‐Ramos, Connor W. Coley and Thijs Stuyver

Chemistry A European J, 2023, 29
DOI: 10.1002/chem.202301957

Machine learning-based yield prediction for transition metal-catalyzed cross-coupling reactions

C. Rajalakshmi, Vivek Vijay, Abhirami Vijayakumar, Shajila Salim, Sherin Susan Cherian, Parvathi Santhoshkumar, John B. Kottooran, Ann Miriam Abraham, G. Krishnaveni, C. S. Anjanakutty, Binuja Varghese and Vibin Ipe Thomas

Theor Chem Acc, 2025, 144
DOI: 10.1007/s00214-024-03159-0