Designing Chemical Reaction Arrays Using Phactor and ChatGPT

BioGPT: generative pre-trained transformer for biomedical text generation and mining

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Bayesian reaction optimization as a tool for chemical synthesis

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ProtGPT2 is a deep unsupervised language model for protein design

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Nanomole-scale high-throughput chemistry for the synthesis of complex molecules

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Expanding the medicinal chemistry synthetic toolbox

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Pre-transmetalation intermediates in the Suzuki-Miyaura reaction revealed: The missing link

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A Multiligand Based Pd Catalyst for C−N Cross-Coupling Reactions

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Palladium-bisimidazol-2-ylidene complexes as catalysts for general and efficient Suzuki cross-coupling reactions of aryl chlorides with arylboronic acids

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Nanoscale synthesis and affinity ranking

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A Multi-Objective Active Learning Platform and Web App for Reaction Optimization

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Automation and computer-assisted planning for chemical synthesis

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Mapping the dark space of chemical reactions with extended nanomole synthesis and MALDI-TOF MS

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The Evolution of Data-Driven Modeling in Organic Chemistry

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Pharmaceutical diversification via palladium oxidative addition complexes

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Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery

Tu, Z., Stuyver, T., & Coley, C. W. (2022) · Chemical Science · doi:10.1039/d2sc05089g · Cited by 141 · see citing works

Ultrahigh-Throughput Experimentation for Information-Rich Chemical Synthesis

Mahjour, B. A., Shen, Y., & Cernak, T. (2021) · Accounts of Chemical Research · doi:10.1021/acs.accounts.1c00119 · Cited by 113 · see citing works

Rapid planning and analysis of high-throughput experiment arrays for reaction discovery

Mahjour, B. A., Zhang, R., Shen, Y., McGrath, A., Zhao, R., Mohamed, O. G., Lin, Y., Zhang, Z., Douthwaite, J. L., Tripathi, A., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-39531-0 · Cited by 88 · see citing works

Predicting reaction conditions from limited data through active transfer learning

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Autonomous platforms for data-driven organic synthesis

Gao, W., Raghavan, P., & Coley, C. W. (2022) · Nature Communications · doi:10.1038/s41467-022-28736-4 · Cited by 70 · see citing works

Synthesis and characterization of N-heterocyclic carbene-palladium( ii ) chlorides-1-methylindazole and -1-methylpyrazole complexes and their catalytic activity toward C–N coupling of aryl chlorides

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Evidence for “cocktail”-type catalysis in Buchwald–Hartwig reaction. A mechanistic study

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Evidence for the “cocktail” nature of platinum-catalyzed alkyne and alkene hydrosilylation reactions

Ondar, E. E., Burykina, J. V., & Ananikov, V. P. (2022) · Catalysis Science & Technology · doi:10.1039/d1cy02006d · Cited by 22 · see citing works

A Highly Efficient Monophosphine Ligand for Parts per Million Levels Pd‐Catalyzed Suzuki–Miyaura Coupling of (Hetero)Aryl Chlorides

Choy, P. Y., Yuen, O. Y., Leung, M. P., Chow, W. K., & Kwong, F. Y. (2020) · European Journal of Organic Chemistry · doi:10.1002/ejoc.202000068 · Cited by 20 · see citing works

A tertiary phosphine oxide ligand-based recyclable system for the Suzuki–Miyaura and Negishi reactions: evidence for pseudo-homogeneous catalysis

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Large language models (LLMs): survey, technical frameworks, and future challenges

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Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering

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ChatMOF: an artificial intelligence system for predicting and generating metal-organic frameworks using large language models

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Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey

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Large language models for reticular chemistry

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Dataset Design for Building Models of Chemical Reactivity

Raghavan, P., Haas, B. C., Ruos, M. E., Schleinitz, J., Doyle, A. G., Reisman, S. E., Sigman, M. S., & Coley, C. W. (2023) · ACS Central Science · doi:10.1021/acscentsci.3c01163 · Cited by 97 · see citing works

Rapid planning and analysis of high-throughput experiment arrays for reaction discovery

Mahjour, B. A., Zhang, R., Shen, Y., McGrath, A., Zhao, R., Mohamed, O. G., Lin, Y., Zhang, Z., Douthwaite, J. L., Tripathi, A., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-39531-0 · Cited by 88 · see citing works

Top 20 influential AI-based technologies in chemistry

Ananikov, V. P. (2024) · Artificial Intelligence Chemistry · doi:10.1016/j.aichem.2024.100075 · Cited by 62 · see citing works

Artificial Intelligence Meets Laboratory Automation in Discovery and Synthesis of Metal–Organic Frameworks: A Review

Zhao, Y., Zhao, Y., Wang, J., & Wang, Z. (2025) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.4c04636 · Cited by 41 · see citing works

Opportunities for Machine Learning and Artificial Intelligence to Advance Synthetic Drug Substance Process Development

Griffin, D. J., Coley, C. W., Frank, S. A., Hawkins, J. M., & Jensen, K. F. (2023) · Organic Process Research & Development · doi:10.1021/acs.oprd.3c00229 · Cited by 40 · see citing works

Using ChatGPT for Method Development and Green Chemistry Education in Upper-Level Laboratory Courses

Ruff, E. F., Franz, J. L., & West, J. K. (2024) · Journal of Chemical Education · doi:10.1021/acs.jchemed.4c00193 · Cited by 45 · see citing works

LLMs' Capabilities at the High School Level in Chemistry: Cases of ChatGPT and Microsoft Bing ChatPreprint

Dao, X.-Q., Le, N.-B., The-Duy, V., Bac-Bien, N., & Phan, X.-D. (2023) · ChemRxiv · doi:10.26434/chemrxiv-2023-kxxpd · Cited by 37 · see citing works

Integrating Machine Learning and Large Language Models to Advance Exploration of Electrochemical Reactions

Zheng, Z., Florit, F., Jin, B., Wu, H., Li, S., Nandiwale, K. Y., Salazar, C. A., Mustakis, J. G., Green, W. H., & Jensen, K. F. (2024) · Angewandte Chemie International Edition · doi:10.1002/anie.202418074 · Cited by 36 · see citing works

Quality by digital design to accelerate sustainable medicines development

Mustoe, C. L., Turner, A., Urwin, S. J., Houson, I., Feilden, H., Markl, D., Qaraghuli, M. M. A., Chong, M. W. S., Robertson, M. N., Nordon, A., et al. (2025) · International Journal of Pharmaceutics · doi:10.1016/j.ijpharm.2025.125625 · Cited by 32 · see citing works

An Investigation of Change in Prompting Strategies in a Semester-Long Course on the Use of GenAI

Sigot, M., & Tassoti, S. (2025) · Journal of Chemical Education · doi:10.1021/acs.jchemed.4c01287 · Cited by 21 · see citing works

Automated approaches, reaction parameterisation, and data science in organometallic chemistry and catalysis: towards improving synthetic chemistry and accelerating mechanistic understanding

Smith, S. C., Horbaczewskyj, C. S., Tanner, T. F. N., Walder, J. J., & Fairlamb, I. J. S. (2024) · Digital Discovery · doi:10.1039/d3dd00249g · Cited by 20 · see citing works

The Future of ChatGPT in Medicinal Chemistry: Harnessing AI for Accelerated Drug Discovery

Pradhan, T., Gupta, O., & Chawla, G. (2024) · ChemistrySelect · doi:10.1002/slct.202304359 · Cited by 17 · see citing works

Rethinking chemical research in the age of large language models

MacKnight, R., Boiko, D. A., Regio, J. E., Gallegos, L. C., Neukomm, T. A., & Gomes, G. (2025) · Nature Computational Science · doi:10.1038/s43588-025-00811-y · Cited by 17 · see citing works

Data-driven recommendation of agents, temperature, and equivalence ratios for organic synthesis

Sun, X., Liu, J., Mahjour, B. A., Jensen, K. F., & Coley, C. W. (2025) · Chemical Science · doi:10.1039/d5sc04957a · Cited by 11 · see citing works

Modulating the Potency of BRD4 PROTACs at the Systems Level with Amine-Acid Coupling Reactions

McGrath, A., Huang, H., Brazeau, J.-F., Zhang, Z., Audu, C. O., Vellore, N. A., Zhu, L., Shi, Z., Venable, J. D., Gelin, C. F., et al. (2024) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.4c02047 · Cited by 11 · see citing works

Rise of the machines: trends and challenges of implementing AI in biomedical scientific writing

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Integrating Machine Learning and Large Language Models to Advance Exploration of Electrochemical Reactions

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Performance of chatbots in queries concerning fundamental concepts in photochemistry

Taniguchi, M., & Lindsey, J. S. (2024) · Photochemistry and Photobiology · doi:10.1111/php.14037 · Cited by 6 · see citing works

Can Reasoning Power Significantly Improve the Knowledge of Large Language Models for Chemistry?─Based on Conversations with LLMs

Cui, D.-X., Long, S.-Y., Tang, Y., Zhao, Y., & Li, Q. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c01265 · Cited by 6 · see citing works

Accelerating compound synthesis in drug discovery: the role of digitalisation and automation

Nippa, D. F., Boddy, A. J., Atz, K., Grether, U., Binch, H., & Martin, R. E. (2025) · RSC Medicinal Chemistry · doi:10.1039/d5md00672d · Cited by 6 · see citing works

Integrating Machine Learning and Large Language Models to Advance Exploration of Electrochemical ReactionsPreprint

Zheng, Z., Florit, F., Jin, B., Wu, H., Li, S., Nandiwale, K. Y., Salazar, C. A., Mustakis, J. G., Green, W. H., & Jensen, K. F. (2024) · ChemRxiv · doi:10.26434/chemrxiv-2024-pk105-v2 · Cited by 3 · see citing works

Comparing large language model artificial intelligence tools in aid of electrical engineering

Tafferner, Z., & Géczy, A. (2024) · IEEE International Symposium for Design and Technology in Electronic Packaging (SIITME) · doi:10.1109/siitme63973.2024.10814860 · Cited by 3 · see citing works

Solving Advanced Task-Specific Problems in Measurement Sciences with Generative AI

Wahab, M. F., Handlovic, T. T., Roy, S., Burk, R. J., & Armstrong, D. W. (2024) · Analytical Chemistry · doi:10.1021/acs.analchem.4c01734 · Cited by 3 · see citing works

Diverse Amine-Acid Coupling Reactions Modulate the Potency of BRD4 PROTACsPreprint

McGrath, A., Huang, H., Brazeau, J.-F., Zhang, Z., Vellore, N. A., Zhu, L., Shi, Z., Venable, J. D., Gelin, C. F., & Cernak, T. (2023) · ChemRxiv · doi:10.26434/chemrxiv-2023-bh0d1 · Cited by 3 · see citing works

High‐Throughput Experimentation Reactor and Modular Environment for Synthesis: Design of a 3D‐Printed Photoreactor for Accessible High‐Throughput Experimentation

Castillo‐Pazos, D. J., Guay, J., Logozzo, A., Lasso, J. D., Lefebvre, L., Kieran, A., Preston, T. C., & LI, C. ‐. (2025) · Chemistry - Methods · doi:10.1002/cmtd.202500110 · Cited by 2 · see citing works

Generative AI in the Service of Product Management: How Generative Technologies Innovate the Product Processes

Witkowski, A., & Wodecki, A. (2025) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-83549-105-820251003 · Cited by 2 · see citing works

Baldwin and Whitehead’s Manzamine Alkaloids Biosynthesis Hy-pothesis Involves a Finely Tuned Reactivity of C3 Unit: a High-Throughput Experimentation ApproachPreprint

Leblond, A., Nguyen, A., Alcover, C., Leblanc, K., Gallard, J., Joseph, D., Poupon, E., & Beniddir, M. A. (2024) · ChemRxiv · doi:10.26434/chemrxiv-2022-nk2cs-v2 · Cited by 2 · see citing works

Accelerate Your Science: Direct-to-Biology Strategies in Medicinal Chemistry

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Exploring the potential of ChatGPT in guiding and troubleshooting the 3D ceramic printing by extrusion free-forming

Sirisathitkul, C., Thongsamrit, W., Noonsuk, W., & Sirisathitkul, Y. (2025) · Arab Journal of Basic and Applied Sciences · doi:10.1080/25765299.2025.2533553 · Cited by 1 · see citing works

Integrating Machine Learning and Large Language Models to Advance Wu Exploration of Electrochemical ReactionsPreprint

Zheng, Z., Florit, F., Jin, B., Wu, H., Li, S., Nandiwale, K. Y., Salazar, C. A., Mustakis, J. G., Green, W. H., & Jensen, K. F. (2024) · ChemRxiv · doi:10.26434/chemrxiv-2024-pk105

ChatGPT Generated Content and Similarity Index in Chemistry & Allied SciencesPreprint

Kirtania, D. K. (2023) · ChemRxiv · doi:10.26434/chemrxiv-2023-vxpk5

La revolución quimioinformática: enseñándole química a los robots

Bastidas, J. R. M. (2024) · Revista de Química · doi:10.18800/quimica.202401.001

Large language models as proxies for human experts in synthesis optimization

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Artificial Intelligence in the Preparation of Scientific Articles

Aldoshin, S. M., Ananikov, V. P., Beletskaya, I. P., Berlin, A. A., Boinovich, L. B., Bukhtiyarov, V. I., Voshkin, A. A., Gorbunova, Y. G., Григорович, К. В., Dzhemilev, U. M., et al. (2026) · Russian Chemical Reviews · doi:10.59761/rcr5247

BindingDB Entry 50022905: Modulating the Potency of BRD4 PROTACs at the Systems Level with Amine-Acid Coupling Reactions.

BindingDB (2026) · UC San Diego · doi:10.7270/q2kd23rw

High-Throughput Experimentation in Practice: Hidden Bottlenecks and Digital Gaps

Cheung, K., Marçon, H. M., Pratley, M. T., Wigh, D. S., Pomberger, A., & Taylor, C. J. (2026) · Organic Process Research & Development · doi:10.1021/acs.oprd.6c00266 · Cited by 1 · see citing works

Rapid planning and analysis of high-throughput experiment arrays for reaction discovery

Mahjour, B. A., Zhang, R., Shen, Y., McGrath, A., Zhao, R., Mohamed, O. G., Lin, Y., Zhang, Z., Douthwaite, J. L., Tripathi, A., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-39531-0 · Cited by 88 · see citing works

ChemReactSeek: an artificial intelligence-guided chemical reaction protocol design using retrieval-augmented large language models

Gong, Z., Zhang, C., Song, D., Xia, W., Shen, B., Su, W. K., Duan, H., & Su, A. (2025) · Chemical Communications · doi:10.1039/d5cc03155a · Cited by 3 · see citing works

Accelerated end-to-end chemical synthesis development with large language models

Ruan, Y., Lu, C., Xu, N., Zhang, J., Xuan, J., Pan, J., Fang, Q., Gao, H., Shen, X., Ye, N., et al. (2024) · doi:10.26434/chemrxiv-2024-6wmg4 · Cited by 140 · see citing works

Artificial-Intelligence-Driven Design and Digital Execution of Chemical Reactions: A Review of Modern Software Platforms and Autonomous Workflows

Gao, S. (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.20637288

Autonomous chemical research with large language models

Boiko, D. A., MacKnight, R., Kline, B., & Gomes, G. (2023) · Nature · doi:10.1038/s41586-023-06792-0 · Cited by 1,050 · see citing works