Benjamin J. Shields, Ph.D.

Benjamin J. Shields, Ph.D.

Chemist & Machine Learning Engineer

Publications

Discovery of Selective Small-Molecule Ligands of SV2C by AI-Enhanced Virtual Screening and Experimental Validation

Published in BioRxiv, 2026

An virtual screening and experimental campaign that identified multiple novel chemotypes with low-micromolar affinity and marked selectivity for SV2C over SV2A and SV2B, yielding the first selective small-molecule ligands for this Parkinson’s disease target.

Recommended citation: Brueckner, Alexander C.; Martin, Matthew F.; Khuttan, Sheenam; Shields, Benjamin J.; Mittal, Anshumali; Schreiber, Jennifer A.; Salomon-Ferrer, Romelia; Bortolato, Andrea; Salahpour, Ali; Bucher, Meghan L.; Coleman, Jonathan A.; Miller, Gary W. "Discovery of Selective Small-Molecule Ligands of SV2C by AI-Enhanced Virtual Screening and Experimental Validation", BioRxiv, 2026. https://doi.org/10.64898/2026.08.11.744237

The Good, The Bad, and The Ugly of Antibody Design: A Roadmap for Integrating Co-folding with Absolute Binding Free Energy Calculations

Published in ChemRxiv, 2026

We introduce Tango for biologics, an implicit-solvent absolute binding free energy framework that enables whole-protein alchemical transformations between antibody variants without shared scaffolds, and define when AI-derived co-folded structures are sufficient for rigorous affinity prediction.

Recommended citation: Sood, Amogh; Jha, Punit K.; Khuttan, Sheenam; Rufa, Dominic; Shields, Benjamin J.; Brueckner, Alex; Beckwith, Zane; Bortolato, Andrea; Pitman, Mary. "The Good, The Bad, and The Ugly of Antibody Design: A Roadmap for Integrating Co-folding with Absolute Binding Free Energy Calculations", ChemRxiv, 2026. https://doi.org/10.26434/chemrxiv.15004911/v1

On improving experimental binding affinity predictions with synthetic data

Published in BioRxiv, 2026

We extend SAIR with ~80K absolute free energy perturbation calculations and show that carefully curated synthetic structures improve binding affinity prediction models.

Recommended citation: Ryczko, Kevin; Zin, Phyo Phyo; Crivelli-Decker, Jordan; Le, Ly; Jha, Punit K.; Shields, Benjamin J.; Lemos, Pablo; Bandi, Sasaank; van Damme, Maarten; Sood, Amogh; Huntington, Lee; Pitman, Mary; Ganahl, Martin; Bortolato, Andrea. "On improving experimental binding affinity predictions with synthetic data", BioRxiv, 2026. https://doi.org/10.64898/2026.03.02.708607

SAIR: Enabling Deep Learning for Protein-Ligand Interactions with a Synthetic Structural Dataset

Published in BioRxiv, 2025

We introduce SAIR, the largest public dataset of protein-ligand 3D structures with activity data.

Recommended citation: Lemos, Pablo; Beckwith, Zane; Bandi, Sasaank; van Damme, Maarten; Crivelli-Decker, Jordan; Shields, Benjamin J.; Merth, Thomas; Jha, Punit K.; De Mitri, Nicola; Callahan, Tiffany J.; Nish AJ; Abruzzo, Paul; Salomon-Ferrer, Romelia; Ganahl, Martin. "SAIR, Enabling Deep Learning for Protein-Ligand Interactions with a Synthetic Structural Dataset", BioRxiv, 2025. https://www.biorxiv.org/content/10.1101/2025.06.17.660168v1

MDFit: automated molecular simulations workflow enables high throughput assessment of ligands-protein dynamics

Published in Journal of Computer-Aided Molecular Design, 2024

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

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", Journal of Computer-Aided Molecular Design, 2024. https://link.springer.com/article/10.1007/s10822-024-00564-2

Identifying general reaction conditions by bandit optimization

Published in Nature, 2024

In this work, we report the design, implementation, and application of bandit optimization algorithms 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. "Identifying general reaction conditions by bandit optimization", Nature, 2024. https://www.nature.com/articles/s41586-024-07021-y

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

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

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

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

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 coupling 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

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 available 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

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, 142, 1859–1869. https://pubs.acs.org/doi/10.1021/jacs.0c00781#

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 spectroscopic 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

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

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 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