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Direct C(sp³)–H Cross Coupling Enabled by Catalytic Generation of Chlorine Radicals

Published in Journal of the American Chemical Society, 2016

Here the development of a novel C(sp³)–H cross-coupling platform enabled by the catalytic generation of chlorine radicals by nickel and photoredox catalysis is reported. This work has led to the a large body of new literature. Highlighted in an ACS Select Virtual Issue. One of the most read articles in August and September.

Recommended citation: Shields, Benjamin J.; Doyle, Abigail G. “Direct C(sp3)–H Cross Coupling Enabled by Catalytic Generation of Chlorine Radicals” J. Am. Chem. Soc., 2016, 138, 12719–12722. https://pubs.acs.org/doi/full/10.1021/jacs.6b08397?src=recsys

Mild Redox-Neutral Formylation of Aryl Chlorides through the Photocatalytic Generation of Chlorine Radicals

Published in Angewandte Chemie International Edition, 2017

A novel redox-neutral method for formylation of aryl chlorides is presented. The mild conditions give unprecedented scope from abundant and complex aryl chloride starting materials. Highlighted in Organic Process Research & Development.

Recommended citation: Nielsen, Matthew K.^; Shields, Benjamin J.^; Liu, Junyi; Williams, M. J.; Zacuto, M. J.; Doyle, Abigail G. “Mild Redox-Neutral Formylation of Aryl Chlorides through the Photocatalytic Generation of Chlorine Radicals” Angew. Chem. Int. Ed. 2017, 56 7191–7194. ^Equal contributions. https://onlinelibrary.wiley.com/doi/abs/10.1002/anie.201702079

Long-Lived Charge Transfer States of Nickel(II) Aryl Halide Complexes Facilitate Bimolecular Photoinduced Electron Transfer

Published in Journal of the American Chemical Society, 2018

This paper summarizes a synthetic, computational, and ultrafast spectroscopyic study of Ni(II) complexes common to cross-coupling and Ni/photoredox reactions. Computational and ultrafast spectroscopic studies reveal that these complexes feature long-lived excited states, implicating Ni as an underexplored alternative to precious metal photocatalysts.

Recommended citation: Shields, Benjamin J.; Kudisch, Bryan; Scholes, Gregory, D.; Doyle, Abigail G. “Long-Lived Charge Transfer States of Nickel(II) Aryl Halide Complexes Facilitate Bimolecular Photoinduced Electron Transfer” J. Am. Chem. Soc., 2018, 140, 3035–3039. https://pubs.acs.org/doi/10.1021/jacs.7b13281

³d-d Excited States of Ni(II) Complexes Relevant to Photoredox Catalysis, Spectroscopic Identification and Mechanistic Implications

Published in Journal of the American Chemical Society, 2020

Building on our previous work, we spectroscopically investigate the long-lived state’s of Ni(II) aryl halide complexes. Ultrafast UV-Vis and mid-IR transient absorption data suggest that a MLCT state is generated initially upon excitation, but decays to a long-lived state that is ³d-d in character.

Recommended citation: Ting, Stephen I.; Garakyaraghi, Sofia; Taliaferro, Chelsea M.; Shields, Benjamin J.; Scholes, Gregory D.; Castellano, Felix N.; and Doyle, Abigail G. “3d-d Excited States of Ni(II) Complexes Relevant to Photoredox Catalysis, Spectroscopic Identification and Mechanistic Implications” J. Am. Chem. Soc., 2020, xxx, xxx–xxx. https://pubs.acs.org/doi/10.1021/jacs.0c00781#

Nickel/Photoredox-Catalyzed Methylation of (Hetero)aryl Chlorides Using Trimethyl Orthoformate as a Methyl Radical Source

Published in Journal of the American Chemical Society, 2020

We report a radical approach to the methylation of (hetero)aryl chlorides using a widely availible solvent as the methyl source.

Recommended citation: Kariofillis, Stavros K.; Shields, Benjamin J.; Tekle-Smith, Makeda; Zacuto, Michael. J.; Doyle, Abigail G. “Nickel/Photoredox-Catalyzed Methylation of (Hetero)aryl Chlorides Using Trimethyl Orthoformate as a Methyl Radical Source” J. Am. Chem. Soc., 2020, 142, 7683–7689. https://pubs.acs.org/doi/pdf/10.1021/jacs.0c02805

Regioselective Cross-Electrophile Coupling of Epoxides and (Hetero)aryl Iodides via Ni/Ti/Photoredox Catalysis

Published in ACS Catalysis, 2020

We report a novel cross-electrophile compling reaction of epoxides enabled by Ni-, Ti-, and photoredox catalysis.

Recommended citation: Parasram, Marvin; Shields, Benjamin J.; Ahmad, Omar; Knauber, Thomas; Doyle, Abigail G. “Regioselective Cross-Electrophile Coupling of Epoxides and (Hetero)aryl Iodides via Ni/Ti/Photoredox Catalysis” ACS Catalysis, 2020, 10, 5821–5827. https://pubs.acs.org/doi/10.1021/acscatal.0c01199

Bayesian reaction optimization as a tool for chemical synthesis

Published in Nature, 2021

Here we report the development of a framework for Bayesian reaction optimization and an open-source software tool that allows chemists to easily integrate state-of-the-art optimization algorithms into their everyday laboratory practices.

Recommended citation: Shields, Benjamin J.; Stevens, Jason; Li, Jun; Parasram, Marvin; Damani, Farhan; Martinez Alvarado, Jesus; Janey, Jacob; Adams, Ryan; Doyle, Abigail G. "Bayesian Reaction Optimization as A Tool for Chemical Synthesis", Nature, 2021, 590, 89–96. https://www.nature.com/articles/s41586-021-03213-y

Predicting Reaction Yields via Supervised Learning

Published in Accounts of Chemical Research, 2021

In this Account, we present a review and perspective on three studies conducted by our group where ML models have been employed to predict reaction yield.

Recommended citation: Zuranski, Andrzej M.; Martinez Alvarado, Jesus; Shields, Benjamin J.; Doyle, Abigail G. "Predicting Reaction Yields via Supervised Learning", Acc. Chem. Res., 2021, 54, 1856–1865. https://pubs.acs.org/doi/10.1021/acs.accounts.0c00770?ref=pdf

Auto-QChem an automated workflow for the generation and storage of DFT calculations for organic molecules

Published in Reaction Chemistry & Engineering, 2022

This perspective describes Auto-QChem, an automatic, high-throughput and end-to-end DFT calculation workflow that computes chemical descriptors for organic molecules.

Recommended citation: Zuranski, Andrzej M.; Wang, J. Y.; Shields, Benjamin J.; Doyle, Abigail G. "Auto-QChem an automated workflow for the generation and storage of DFT calculations for organic molecules", React. Chem. Eng., 2022, 7, 1276. https://doi.org/10.1039/D2RE00030J

Reinforcement learning prioritizes general applicability in reaction optimization

Published in ChemRxiv, 2023

In this work, we report the design, implementation, and application of reinforcement learning bandit optimization models to identify generally applicable conditions in a variety of chemical transformations.

Recommended citation: Wang, Jason Y.; Stevens, Jason M.; Kariofillis, Stavros K.; Tom Mai-Jan; Li, Jun; Tabora, Jose E.; Parasram, Marvin; Shields, Benjamin J.; Primer, David; Hao, Bo; Valle, David D.; DiSomma, Stacey; Furman, Ariel; Zipp, Greg G.; Melnikov, Sergey; Paulson, James; Doyle, Abigail G. "Reinforcement learning prioritizes general applicability in reaction optimization", ChemRxiv, 2023. https://doi.org/10.26434/chemrxiv-2023-dcg9d

Scoring Methods in Lead Optimization of Molecular Glues

Published in ChemRxiv, 2023

Molecular glue compounds are characterized by the potency and the depth of their protein degradation dose response measurement, representing additional complexity toward identifying drug candidates. We developed degradation efficiency metrics that are based on both potency and depth of degradation. They serve as basic scoring functions to effectively track lead optimization objectives.

Recommended citation: Jia, Lei; Weiss, Dahlia; Shields, Benjamin J.; Claus, Brian; Shanmugasundaram, Veerabahu; Johnson, Stephen; Riggs, Jennifer; Zapf, Christoph "Scoring Methods in Lead Optimization of Molecular Glues", ChemRxiv, 2023. https://doi.org/10.26434/chemrxiv-2023-4hn4s

MDFit, Automated molecular simulations workflow enables high throughput assessment of ligands-protein dynamics

Published in ChemRxiv, 2024

We present an automated workflow that streamlines setting up, running, and analyzing Desmond MD simulations.

Recommended citation: Bruechner, Alexander; Shields, Benjamin J.; Kirubakaran, Palani; Suponya, Alexander; Panda, Manoranjan; Posy, Shana; Johnson, Stephen; Lakkaraju, Sirish K. "MDFit, Automated molecular simulations workflow enables high throughput assessment of ligands-protein dynamics", ChemRxiv, 2024. https://doi.org/10.26434/chemrxiv-2024-gfcqx

Machine learning in methods development: From reaction outcome prediction to mechanistic understanding

Published: May 01, 2019

Machine learning (ML), the development and study of computer algorithms that can learn from data, is increasingly important across a wide array of applications in chemistry. For example, ML has facilitated virtual screening of druglike molecules for medical applications, rapid prediction of physical data, and computer aided synthesis planning. While ML has become well-established in these areas, scientists have only just begun to advance tools for synthetic methods development (reaction optimization, prediction, mechanistic study). Though these burgeoning areas of research have already added to the synthetic chemist’s toolbox, average research practices have remained relatively unaffected. One approach to facilitating the adoption of ML in synthetic chemistry is to develop applications which integrate seamlessly with the typical methods of synthetic chemists. Here I will discuss approaches to some obstacles to incorporating ML in the synthetic mainstay including: (1) interpretability – scientists may not trust a model because predictions appear to be unintelligible or derived randomly from regressors. This challenge could be overcome by using simple interpretable graphics and traditional physical organic chemistry to explain and experimentally probe ML results. (2) Data – current approaches to applying ML in synthetic chemistry have focused on mining the chemical literature or actively generating new datasets on a per problem basis. However, mined data is sparse, noisy, and often incomplete and data set curation imposes a heavy experimental cost. An alternative approach is to draw from the success of ML in other areas which incorporate data endogenous to a given domain (e.g. product recommendation systems). Much of the data collected in synthetic chemistry laboratories is derived from the optimization of reactions. While this data is typically leveraged only towards the discovery of optimal conditions, a method which draws from optimization data, quantum chemical calculations, and ML could naturally integrate with synthetic research practices.

Bayesian optimization as an approach to drug development

Published: July 06, 2021

Optimization is ubiquitous in pharmaceutical development, from tuning chemical structure to maximize potency to optimizing the yield of a chemical process. Likewise, parameter optimization is omnipresent in artificial intelligence, from tuning virtual personal assistants to training social media and product recommendation systems. Owing to the high cost associated with carrying out experiments, scientists in both areas set numerous (hyper)parameter values by evaluating only a small subset of the possible configurations. Bayesian optimization, an iterative response surface-based global optimization algorithm, has demonstrated exceptional performance in the tuning of machine learning models. Here we report the development of a framework for Bayesian reaction optimization and an open-source software tool that allows chemists to easily integrate state-of-the-art optimization algorithms into their everyday laboratory practices. We collect a large benchmark dataset for a palladium-catalysed direct arylation reaction, perform a systematic study of Bayesian optimization compared to human decision-making in reaction optimization, and apply Bayesian optimization to two real-world optimization efforts (Mitsunobu and deoxyfluorination reactions). Benchmarking is accomplished via an online game that links the decisions made by expert chemists and engineers to real experiments run in the laboratory. Our findings demonstrate that Bayesian optimization outperforms human decision making in both average optimization efficiency (number of experiments) and consistency (variance of outcome against initially available data). Overall, our studies suggest that adopting Bayesian optimization methods into everyday laboratory practices could facilitate more efficient synthesis of functional chemicals by enabling better-informed, data-driven decisions about which experiments to run.

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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