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Harnessing surrogate models for data-efficient predictive chemistry: descriptors vs. learned hidden representations

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Bisphosphine ligand conformer selection to enhance descriptor database representation: improving statistical modelling outcomes

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Rapid prediction of conformationally-dependent DFT-level descriptors using graph neural networks for carboxylic acids and alkyl amines

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Constructing and explaining machine learning models for the exploration and design of boron-based Lewis acids

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Incorporating Synthetic Accessibility in Drug Design: Predicting Reaction Yields of Suzuki Cross-Couplings by Leveraging AbbVie’s 15-Year Parallel Library Data Set

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Machine Learning for Developing Sustainable Polymers

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Data Science-Driven Discovery of Optimal Conditions and a Condition-Selection Model for the Chan–Lam Coupling of Primary Sulfonamides

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ACS Catal., 2025, 15, 2292
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A General Photocatalytic Strategy for Nucleophilic Amination of Primary and Secondary Benzylic C–H Bonds

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J. Am. Chem. Soc., 2023, 145, 18487
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AQME: Automated quantum mechanical environments for researchers and educators

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WIREs Comput Mol Sci, 2023, 13
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Integrating digital chemistry within the broader chemistry community

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Branched-Selective Cross-Electrophile Coupling of 2-Alkyl Aziridines and (Hetero)aryl Iodides Using Ti/Ni Catalysis

Wendy L. Williams, Neyci E. Gutiérrez-Valencia and Abigail G. Doyle

J. Am. Chem. Soc., 2023, 145, 24175
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Reaction-Agnostic Featurization of Bidentate Ligands for Bayesian Ridge Regression of Enantioselectivity

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ACS Catal., 2024, 14, 9302
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Considerations in Pursuing Reaction Scope Generality

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Angew Chem Int Ed, 2025, 64
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Small Data Can Play a Big Role in Chemical Discovery

Hadas Shalit Peleg and Anat Milo

Angewandte Chemie, 2023, 135
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Classifying Host–Guest Topology with Ion Mobility-Mass Spectrometry and Machine Learning

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Small Data Can Play a Big Role in Chemical Discovery

Hadas Shalit Peleg and Anat Milo

Angew Chem Int Ed, 2023, 62
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MetaRF: attention-based random forest for reaction yield prediction with a few trails

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J Cheminform, 2023, 15
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NOCTIS: open-source toolkit that turns reaction data into actionable graph networks

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ChemRefine: An Open-Source Automated and Interoperable Platform for Machine Learning and Quantum Chemistry Simulations

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Data Science-Enabled Palladium-Catalyzed Enantioselective Aryl-Carbonylation of Sulfonimidamides

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

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J. Am. Chem. Soc., 2023, 145, 7898
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Machine Learning in Homogeneous Catalysis: Basic Concepts and Best Practices

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ACS Catal., 2026, 16, 1
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Tetramethylchloroformamidinium Hexafluorophosphate–N-Methylimidazole Amidation in Water: Successes, Limitations, and a Regression Model for Prediction

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

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Decoding Regioselectivity in Cu-Catalyzed Borylation of Alkynes: Insights from Machine Learning and Artificial Intelligence

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ACS Catal., 2025, 15, 20230
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Development of a Heteroleptic Palladium Catalyst for the Fluorination of Arylboronic Acids using Data Science Tools

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Automated DFT-Machine Learning Integration Enables Data-Efficient and Generalizable Feasibility Predictions in Metallaphotoredox sp2–sp3 Cross-Coupling Reactions

Michele Assante, Sara Pahlen, Brenda Pijper, Lili Cao, Ole Specht, Nadja Simeth, Alexei Lapkin, Clive Green, Ola Engkvist, Giulia Bergonzini and Mikhail Kabeshov

ACS Catal., 2026, 16, 3547
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Autonomous Synthesis and Inverse Design of Electrochromic Polymers with High Efficiency and Accuracy

Yukun Wu, Aikaterini Vriza, Doga Ozgulbas, Rafael Vescovi, Jianing Zhou, Zhiyang Wang, Shiyu Hu, Yuepeng Zhang, Qiaomu Yang, Anna M. Österholm, John R. Reynolds, Subramanian K. R. S. Sankaranarayanan, Maria K. Y. Chan, Ian T. Foster, Jianguo Mei, Henry Chan and Jie Xu

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

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Fully autonomous materials screening methodology combining first-principles calculations, machine learning and high-performance computing system

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Science and Technology of Advanced Materials: Methods, 2023, 3
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Revealing the Relationship between Publication Bias and Chemical Reactivity with Contrastive Learning

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Bottom-Up Atomistic Descriptions of Top-Down Macroscopic Measurements: Computational Benchmarks for Hammett Electronic Parameters

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Predicting Highly Enantioselective Catalysts Using Tunable Fragment Descriptors**

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Integrating Data Science and Machine Learning with an Aldol Condensation Laboratory

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Paving the road towards automated homogeneous catalyst design

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Machine learning-guided strategies for reaction conditions design and optimization

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Considerations in Pursuing Reaction Scope Generality

Samson B. Zacate, Juliana A. Dantas, Song Lin, Abigail G. Doyle and Matthew S. Sigman

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Multimodal descriptor integration and transfer learning for rapid yet accurate prediction metallocene ethylene-insertion activity

Tao Yang, Qiuyi Wu, Jie Liang, Yao Qin, Xiao Wei, Yuan Wang, Daheng Wen, Pengfei Jia, Yuanqin Zhu, Shuangliang Zhao and Zengxi Wei

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Self-Driving Laboratories for Chemistry and Materials Science

Gary Tom, Stefan P. Schmid, Sterling G. Baird, Yang Cao, Kourosh Darvish, Han Hao, Stanley Lo, Sergio Pablo-García, Ella M. Rajaonson, Marta Skreta, Naruki Yoshikawa, Samantha Corapi, Gun Deniz Akkoc, Felix Strieth-Kalthoff, Martin Seifrid and Alán Aspuru-Guzik

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Predicting Highly Enantioselective Catalysts Using Tunable Fragment Descriptors**

Nobuya Tsuji, Pavel Sidorov, Chendan Zhu, Yuuya Nagata, Timur Gimadiev, Alexandre Varnek and Benjamin List

Angewandte Chemie, 2023, 135
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