About

Abigail G. Doyle is a professor and the Saul Winstein Endowed Chair in Organic Chemistry at UCLA. She received her A.B. and A.M. summa cum laude in Chemistry and Chemical Biology from Harvard University in 2002, followed by a PhD from the same department in 2008. She began her independent academic career in the Department of Chemistry at Princeton University in 2008 and moved to UCLA in 2021. Her research focuses on the interface of organic, organometallic, physical organic, and computational chemistry, aiming to solve unsolved problems in organic synthesis through the development of catalysts, catalytic reactions, and synthetic methods. Her work employs mechanistic and computer-assisted techniques to analyze reactions, uncover general principles, and guide the design of improved catalysts and discovery of new reactions.

Research topics

• Computer Science
• Chemistry
• Artificial Intelligence
• Machine Learning
• Engineering
• Organic chemistry
• Nanotechnology
• Programming language
• Data science
• Photochemistry
• Information Retrieval
• Materials science
• Biochemical engineering
• Algorithm
• Software engineering
• Biology
• Atomic physics
• Database
• Ecology
• World Wide Web

Selected publications

• Markovnikov hydroamination of terminal alkenes by phosphine redox catalysis

  Nature · 2026-02-23 · 1 citations

  articleOpen accessSenior author

  Abstract Main-group catalysts that mimic transition metal reactivity can expand substrate tolerance and enable transformations not possible at present with metal catalysis 1 . The discovery that P III and P V phosphorus intermediates can undergo transition-metal-like two-electron chemistry raises the question of whether radical P IV intermediates can mimic other elementary steps in organometallic chemistry 2,3 . Here we describe a phosphine–photoredox catalyst system that promotes intermolecular Markovnikov hydroamination of unactivated terminal alkenes with numerous classes of N–H azoles, a reaction that is not possible with late transition metal catalysis. Experimental and computational mechanistic studies support a new elementary step for main-group catalysis, in which a phosphine radical cation activates the alkene to nucleophilic amination by the azole, a step otherwise associated with transition metals. Given the broad value of nucleophilic alkene functionalization in transition metal catalysis, this P IV mechanism could offer new opportunities for main-group element catalysis and chemical synthesis.

  PublisherDOI

• Synergizing Chemical and AI Communities for Advancing Laboratories of the Future

  ACS Central Science · 2026-01-27

  articleOpen access

  The development of automated experimental facilities and the digitization of experimental data have introduced numerous opportunities to radically advance chemical laboratories. As many laboratory tasks involve predicting and understanding previously unknown chemical relationships, machine learning (ML) approaches trained on experimental data can substantially accelerate the conventional design-build-test-learn process. This outlook article aims to help chemists understand and begin to adopt ML predictive models for a variety of laboratory tasks, including experimental design, synthesis optimization, and materials characterization. Furthermore, this article introduces how artificial intelligence (AI) agents based on large language models can help researchers acquire background knowledge in chemical or data science and accelerate various aspects of the discovery process. We present three case studies in distinct areas to illustrate how ML models and AI agents can be leveraged to reduce time-consuming experiments and manual data analysis. Finally, we highlight existing challenges that require continued synergistic effort from both experimental and computational communities to address.

  PublisherDOI

• Integrating Data Science and Machine Learning with an Aldol Condensation Laboratory

  Journal of Chemical Education · 2026-02-26

  articleSenior authorCorresponding

  We report the development of an undergraduate organic chemistry laboratory to introduce students to modern applications of data science tools and machine learning algorithms in organic chemistry. Data science and machine learning have become increasingly applied to organic chemistry systems built upon physical organic principles of reactivity to better analyze and interpret data. Given that postexperimental analysis is central to any scientific study, we envision that the incorporation of these techniques at an introductory level into the undergraduate chemistry education curriculum will be invaluable in exposing students to contemporary research tools and working with shared data. Herein we describe a two-part experiment, using the experimentally straightforward Claisen–Schmidt aldol condensation reaction with commercially available reagents, to introduce concepts of computational featurization and data processing for multivariate linear regression models at the undergraduate level that can easily be incorporated into organic instructional laboratories.

  PublisherDOI

• Transferable enantioselectivity models from sparse data

  Nature · 2026-02-11

  articleOpen access

  Abstract Identifying a catalyst class to optimize the enantioselectivity of a new reaction, either involving a different combination of known substrate types or an entirely unfamiliar class of compounds, is a formidable challenge. Statistical models trained on a reported set of reactions can help predict out-of-sample transformations 1–5 but often face two challenges: (1) only sparse data that offer limited information on catalyst–substrate interactions are available; and (2) simple stereoelectronic parameters may fail to describe mechanistically complex transformations 6,7 . Here we report a descriptor generation strategy that accounts for changes in the enantiodetermining step with catalyst or substrate identity, allowing us to model reactions involving distinct ligand and substrate types. As validating case studies, we collected data on enantioselective nickel-catalysed C( sp 3 ) couplings 8 and trained statistical models with features extracted from the transition states and intermediates proposed to be involved in asymmetric induction. These models allow the optimization of poorly performing examples reported in a substrate scope and are applicable to unseen ligands and reaction partners. This approach offers the opportunity to streamline catalyst and reaction development, quantitatively transferring knowledge learned on sparse data to chemical spaces.

  PublisherDOI

• Scalable Enantioselective Oxidation of Aliphatic N-heterocycles via Catalytic Hydride Abstraction

  ChemRxiv · 2026-01-29

  articleOpen access

  Enantioselective C-H functionalization remains a highly desired yet challenging goal in organic synthesis. A prevailing strategy involves nonselective C-H cleavage followed by enantioselective functionalization of the resultant prochiral intermediate. A complementary but substantially less explored approach is direct asymmetric C-H activation (e.g., desymmetrization or kinetic resolution), which can generate multiple stereocenters and enable access to complex stereochemical arrays. Here, we report the enantioselective oxidation of N-protected cyclic amines to chiral lactams via asymmetric hydride abstraction by novel oxoammonium catalysts. This process employs a readily prepared amine catalyst precursor and is operationally simple, air-and water-tolerant, and highly selective across a broad substrate scope. This method streamlines the synthesis of key intermediates for bioactive molecules on preparative scale (up to 22 g lab scale and 10 kg plant scale) and provides rapid access to structural analogs of these medicinal agents. Mechanistic studies reveal hydride abstraction to be enantiodetermining and uncover stereoelectronic factors that underpin the observed high enantioselectivity.

  PublisherOA PDFDOI

• Machine Learning-Guided Scope Selection to Balance Performance and Substrate Similarity

  ChemRxiv · 2025-12-22

  articleSenior author

  The determination of a reaction substrate scope enables downstream users to decide whether the reaction in question is suitable for their envisioned application. The information content of the scope, as demonstrated by its performance and diversity, is crucial to inform the quality of this decision. Herein, we report a broadly applicable and easy to use machine learning algorithm, ScopeBO, for the selection of scopes that balance these two aspects. We use the Vendi score as a metric for scope diversity and establish a scope score that quantifies scope performance within the context of a specific chemical search space. The hyperparameters of ScopeBO are optimized using these metrics, and its performance is validated with several reaction datasets, demonstrating favorable performance against that of alternative selection methods. Through this quantitative optimization, ScopeBO provides an approach towards objective and standardized scope selection that maximizes the information content of the evaluated substrates.

  PublisherDOI

• DiploPhos: A Hemilabile Bisphosphine for Sterically Hindered Ni-Catalyzed Suzuki–Miyaura Couplings

  ChemRxiv · 2025-12-23 · 1 citations

  articleSenior author

  Pharmaceutically relevant Suzuki–Miyaura cross-couplings (SMCs) often require designer phosphine ligands and palladium loadings above 1 mol% to couple Lewis basic, sterically congested substrates. Recent work has demonstrat-ed that nickel is an attractive alternative to palladium for facile SMCs, but further ligand development is required for Ni catalysis to rival Pd for more challenging couplings. We applied prior work on monophosphine ligand design for Ni to develop a bisphosphine, DiploPhos, that outperforms state-of-the-art ligands for Ni to achieve sterically hin-dered Ni SMCs. Catalyst speciation studies revealed the hemilabile nature of DiploPhos, which improves reactivity relative to stronger chelating ligands but also leads to the formation of less-active DiploPhos-bridged aggregates. Lew-is basic functionality (present on substrates or additives) was found to promote the disaggregation of these species and led to increased SMC yields. This observation is contrary to most other systems in which Lewis basic substrates inhibit Ni-catalyzed SMC reactions. Ligand exchange studies demonstrated that despite its hemilability, DiploPhos is more resistant to displacement by heterocycles than similar bisphosphines. Together, these properties led to best-in-class reactivity for sterically hindered, Lewis base-rich Ni SMCs.

  PublisherDOI

• Low-Cost Mechanism-Informed Features Enable Transferable Enantioselectivity Predictions from Sparse Data

  ChemRxiv · 2025-07-20 · 1 citations

  preprintOpen access

  Identifying a catalyst class to optimize the enantioselectivity of a new reaction, either involving a different combination of known substrate types or an entirely unfamiliar class of compounds, is a formidable challenge. Statistical models trained on a reported set of reactions can help predict out-of-sample transformations but often face two challenges: (1) only sparse data are available i.e., limited information on catalyst–substrate interactions, and (2) simple stereoelectronic parameters may fail to describe mechanistically complex transformations. Here we report a descriptor generation strategy that accounts for changes in the enantiodetermining step with catalyst or substrate identity, allowing us to model reactions involving distinct ligand and substrate types. As validating case studies, we collected data on enantioselective nickel-catalyzed C(sp3)-couplings and trained statistical models with features extracted from the transition states and intermediates involved in asymmetric induction. These models allow for the optimization of poorly performing examples reported in a substrate scope and are applicable to unseen ligands and reaction partners. This approach offers the opportunity to streamline catalyst and reaction development, quantitatively transferring knowledge learned on sparse data to novel chemical spaces.

  PublisherOA PDFDOI

• Getting under the skin of the menopausal hot flush: a protocol to examine skin function and structure in symptomatic postmenopausal women

  Frontiers in Global Women s Health · 2025-08-04

  articleOpen access

  Introduction: The major pathophysiological symptom of the menopause affecting daily life is hot flushes, which are also associated with elevated cardiovascular disease risk. A hot flush is a sudden and intense heat sensation causing skin flushing and profuse sweating. Menopause-induced oestrogen deficiency could increase the sensitivity of skin blood vessels and sweat glands in postmenopausal women, which could result in more frequent and larger increases in skin blood flow in postmenopausal women consistent with hot flushes. Furthermore, oestrogen withdrawal could also alter the structure of the skin blood vessels and/or sweat glands which may also contribute to hot flushes. This trial aims to examine the function and structure of skin blood vessels and sweat glands in premenopausal and postmenopausal women. Methods and analysis: This is a single-centre multi-cohort observational study. Participants will attend the laboratory at Liverpool John Moores University (LJMU) on two separate occasions, ∼7 days apart. Visit 1 will consist of anthropometry, a blood sample and assessment of post-ganglionic skin blood vessel and sweat gland responsiveness via cutaneous microdialysis. At visit 2, participants will return for a skin punch biopsy. A between groups statistical analysis of the pre- and postmenopausal cohorts will be conducted in a blinded manner. Ethics and dissemination: The trial was approved by the North West - Greater Manchester South Research Ethics Committee (22/NW/0300) in the UK. The study adheres to The Declaration of Helsinki and is being conducted in accordance with the UK Policy Framework for Health and Social Care Research. Discussion: Identifying functional and/or structural changes in skin blood vessels or sweat glands in women with hot flushes would increase our understanding of their cause(s) and side effects, and help to design effective treatments, including interventions that can manipulate the activity of the skin blood vessels and/or sweat glands via pharmacological or non-pharmacological methods. Trial registration numbers: NCT06222073.

  PublisherOA PDFDOI

• Reactivity Studies of Bipyridine-Ligated Nickel(I) and Nickel(0) Complexes Inform Mechanism in Modern Cross-Coupling Reac-tions

  ChemRxiv · 2025-07-06

  preprintOpen accessSenior author

  Bipyridine-ligated nickel(I) and nickel(0) intermediates are widely proposed in Ni-catalyzed cross-coupling reac-tions. However, few isolable NiI and Ni0 complexes with catalytically relevant bipyridine ligands are known, limiting our understanding of these complexes’ speciation and reactivity. In this work, we identify and investigate well-defined, isolable (t-Bubpy)Ni(I) and (t-Bubpy)Ni(0) complexes to characterize their behavior in catalytic systems. Employing spectroscopic and stoichiometric studies, we identified a solvent dependence on rates of the irreversible dimerization of (t-Bubpy)NiBr, measured rates for the activation of aryl halides and alkyl halides by (t-Bubpy)Ni(I) and (t-Bubpy)Ni(0), and found that the reduction of (t-Bubpy)NiBr to Ni(0) is inefficient with common heterogeneous metal reductants. Taken together, these studies enabled us to propose a general mechanism for cross-electrophile coupling reactions.

  PublisherOA PDFDOI

Recent grants

• Metal-Catalyzed Cross Coupling with N,O-Acetals and Acetals

  NIH · $1.5M · 2013–2019

• New Directions in Nickel and Photoredox Catalysis

  NIH · $3.7M · 2018–2028

• Methods for Late-Stage Nucleophilic Fluorination and Radiofluorination

  NSF · $450k · 2016–2020

• CAREER: New Reagents and Strategies for Catalytic Nucleophilic Fluorination

  NSF · $602k · 2012–2017

• (Radio)Fluorination and Other Nucleophilic Functionalizations Enabled by Photocatalytic Radical-Polar Crossover

  NSF · $500k · 2021–2024

Frequent coauthors

• Samuel H. Newman-Stonebraker

  Yale University

  75 shared

• Hootan Roshandel

  University of California, Los Angeles

  29 shared

• Wendy L. Williams

  Princeton University

  22 shared

• Philip D. Jeffrey

  Princeton University

  21 shared

• Jason Y. Wang

  20 shared

• T. Judah Raab

  University of California, Los Angeles

  18 shared

• Benjamin J. Shields

  Princeton University

  17 shared

• Matthew S. Sigman

  University of Utah

  16 shared

Education

• PhD, Chemistry and Chemical Biology

  Harvard University

  2008

• AB, AM, Chemistry and Chemical Biology

  Harvard University

  2002

Awards & honors

• MAVEN Senior Scientist (2024)
• XXI American Chemical Society Fellow (2020)
• RSC Fluorine Award (2019)
• 15th Hirata Prize (2019)
• BMS Unrestricted Grant in Synthetic Organic Chemistry (2016)