About

Adam J. Gormley is an Associate Professor at Rutgers University in the Department of Biomedical Engineering. His research interests include artificial intelligence, machine learning, robotics, nanomaterials, polymers, and self-assembly. He is involved in advancing knowledge and applications in these areas, contributing to the development of innovative biomedical technologies and engineering solutions.

Research topics

• Computer Science
• Nanotechnology
• Chemistry
• Materials science
• Biochemical engineering
• Organic chemistry
• Biochemistry
• Biology
• Engineering
• Combinatorial chemistry
• Database
• Cell biology

Selected publications

• Gene Delivery Mediated by Backbone-Degradable RAFT Copolymers

  Biomacromolecules · 2026-02-12

  articleOpen accessSenior authorCorresponding

  Cationic polymers present an attractive platform for gene delivery. However, these highly charged macromolecules can also lead to cytotoxicity. Therefore, there is a strong unmet need to develop efficacious polymeric gene delivery vehicles with high biocompatibility. Here, we leverage recent advances in polymer chemistry to develop backbone-degradable cationic copolymers and evaluate their potential as gene delivery vehicles. Specifically, polycations were prepared via copolymerization with macrocyclic allylic sulfides, which can participate in PET-RAFT polymerization via radical ring-opening cascade copolymerization to install degradable backbone segments. A polymer library with varying degradabilities was prepared and evaluated using a model GFP plasmid to transfect U-2 OS cells. Incorporation of degradable groups into the copolymer backbone improved transfection efficiency 10-fold at low amine/phosphate (N/P) ratios without increasing cytotoxicity, thereby enhancing their value as gene delivery carriers. We hypothesize that degradability may enhance the complex's disassembly kinetics in the cytosol, enabling more efficient payload release.

  PublisherDOI

• Automated active learning to optimize hydrogel drug release profiles

  Journal of Controlled Release · 2026-01-03 · 1 citations

  articleOpen accessSenior authorCorresponding

  Hydrogels are widely used in drug delivery due to their biocompatibility and tunable release properties. However, optimizing hydrogel formulations to the desired release of therapeutics remains experimentally intensive. In this study, we developed an automated, high-throughput and machine learning (ML)-guided framework to efficiently optimize alginate formulations for drug delivery. Using a liquid handling robot, we initially prepared a diverse seed library of 120 alginate hydrogel formulations loaded with bovine serum albumin (BSA) and measured their release profiles. A Gaussian process regression (GPR) ML model was trained to predict cumulative release across time, enabling implicit modeling of release curves. Feature importance analysis using Shapley additive explanations (SHAP) identified time, alginate molecular weight, and concentration as dominant factors influencing release kinetics. Through Bayesian optimization and active learning, we iteratively selected and tested new formulations, progressively reaching a near zero-order release. Finally, the top-performing BSA-optimized formulations were directly applied to the sustained release of chondroitinase ABC single-enzyme nanoparticles (chABC-SENs), achieving near-zero-order release with no further optimizations. This study demonstrates a scalable, data-driven strategy for hydrogel formulation optimization and highlights the potential of ML to accelerate the development of controlled release systems for sensitive and valuable therapeutics. • Automated gel fabrication allows for efficient characterization of release kinetics. • Active learning accelerates alginate hydrogel drug release optimization. • Bayesian optimization enables near zero-order release after two iterations. • Feature analysis found alginate molecular weight and concentration as key drivers. • Optimized hydrogels achieve sustained chondroitinase ABC enzyme release.

  PublisherDOI

• Gene Delivery Mediated by Backbone-Degradable RAFT Copolymers

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-10-17

  preprintOpen accessSenior authorCorresponding

  Cationic polymers present an attractive platform for gene delivery. However, these highly charged macromolecules can also lead to cytotoxicity. Therefore, there is a strong unmet need to develop efficacious polymeric gene delivery vehicles with high biocompatibility. Here, we leveraged recent advances in polymer chemistry to develop backbone-degradable cationic copolymers and evaluate their potential as gene delivery vehicles. Specifically, polycations were prepared via copolymerization with macrocyclic allylic sulfides which can participate in PET-RAFT polymerization via radical ring-opening cascade copolymerization to install degradable backbone segments. A polymer library with varying degradability was prepared and evaluated using a model GFP plasmid to transfect U-2 OS cells. Incorporation of degradable groups into the copolymer backbone improved transfection efficiency 10-fold at low amine/phosphate (N/P) ratios without increasing cytotoxicity, thereby enhancing their value as gene delivery carriers. We hypothesize that degradability may enhance the complex's disassembly kinetics in the cytosol, enabling more efficient payload release.

  PublisherOA PDFDOI

• Martini 3 coarse-grained model of enzymes: Framework with validation by all-atom simulations and x-ray diffraction measurements

  The Journal of Chemical Physics · 2025-04-03 · 2 citations

  articleOpen access

  Recent experiments have shown that complexation with a stabilizing compound can preserve enzyme activity in harsh environments. Such complexation is believed to be driven by noncovalent interactions at the enzyme surface, including hydrophobicity and electrostatics. Molecular modeling of these interactions is costly at the all-atom scale due to the long time scales and large particle counts needed to characterize binding. Protein structure at the scale of amino acid residues is parsimoniously represented by a coarse-grained model in which one particle represents several atoms, significantly reducing the cost of simulation. Coarse-grained models may then be used to generate reduced surface descriptions to underlie detailed theories of surface adhesion. In this study, we present two coarse-grained enzyme models-lipase and dehalogenase-that have been prepared using the Martini 3 top-down modeling framework. We simulate each enzyme in aqueous solution and calculate the statistics of protein surface features and shape descriptors. The values from the coarse-grained data are compared with the same calculations performed on all-atom reference systems, revealing key similarities of surface chemistry at the two scales. Structural measures are calculated from the all-atom reference systems and compared with estimates from small-angle x-ray scattering experiments, with good agreement between the two. The described procedures of modeling and analysis comprise a framework for the development of coarse-grained models of protein surfaces with validation to experiment.

  PublisherOA PDFDOI

• SAXS Assistant: Automated SAXS analysis for structural discovery in biologics and polymeric nanoparticles

  Biophysical Journal · 2025-09-25 · 1 citations

  articleOpen accessSenior author
  PublisherOA PDFDOI

• Automation and Active Learning for the Multi-Objective Optimization of Antibody Formulations

  ChemRxiv · 2025-10-21 · 1 citations

  articleSenior author

  Over the last forty years, biologics such as monoclonal antibodies have become an increasingly important therapeutic agent in the treatment of numerous diseases. Between 1986 and 2025, over 200 antibody-based treatments have been approved globally, most of which are manufactured as preformulated solutions for subsequent administration to patients. However, bioformulation of complex proteins is a difficult engineering challenge; formulations must be tailored to individual therapies, necessitating time- and material-intensive campaigns to select a combination of excipients to simultaneously optimize an array of design criteria. These many interacting additives complicate formulation design with unintuitive and non-linear relationships, thus creating a vast and multidimensional design space that is intrinsically difficult to optimize using traditional techniques. To address this challenge, we investigated a high-throughput discovery pipeline using machine learning to model and predict formulation behavior of Generally Recognized as Safe (GRAS) excipients on a model antibody. This was supported by automation-assisted “on-demand” formulation to produce dozens of uniquely formulated antibody solutions with high reproducibility for downstream evaluation and biophysical characterization. This pipeline was then integrated into an iterative closed-loop cycle of automated Design-Build-Test-Learn (DBTL), where new rounds of experiments are designed by the ML model. The process yielded both optimized formulations as well as highly accurate predictive models of formulation behavior. This validates the utility of this technique to both map the underlying property-function landscape and effectively guide formulation development while balancing multiple competing design requirements.

  PublisherDOI

• Self-driving lab for the data-driven design of single-chainpolymer nanoparticles

  ChemRxiv · 2025-10-21

  preprintSenior author

  Careful mapping of protein structure-activity relationships connecting amino acid sequence to its tertiary structure has spurred the rise of rational protein design using computational and experimental methods. Drawing inspiration from proteins for synthetic materials, careful choice of monomers enables the creation of novel single-chain polymer nanoparticles (SCNPs) with self-assembling characteristics. Rationally designed SCNPs permit the necessary structural complexity for use as protein mimics and polymer-protein hybrids, while granting access to a broad chemical design space, straightforward preparation, and tunable properties. Advances in oxygen-tolerant photoinduced polymerization chemistries have greatly facilitated efficient preparation of SCNPs. Here, we describe a high-throughput, autonomous workflow for active learning to discover structure-property relationships and use them to iteratively predict and synthesize novel SCNPs. We developed a control system consisting of a liquid handling robot to mix polymerization reagents, a custom-built lightbox to catalyze PET-RAFT polymerization, a dynamic light scattering (DLS) plate reader to characterize polymer hydrodynamic radius (Rh), and a robotic arm to transport polymer-containing well plates from one instrument to another. By first testing low-feature, rationally designed polymer libraries, we gained experimental context for the development of higher-feature, randomly sampled seed libraries as training sets for Gaussian process regressor (GPR) models. Over multiple generations of Bayesian optimization (BO), additional generations of polymer synthesis were found not only to successfully improve model performance but also to represent the impact of specific monomer content on Rh. This automated polymer discovery platform serves as a useful prototype for SCNP design using more complex design features as well as more advanced optimization targets, including polymers whose structure can be tailored for enzyme stabilization and other biomedical applications.

  PublisherDOI

• A User's Guide to Your First Self-Driving Liquid Handling Lab

  ChemRxiv · 2025-10-23

  articleOpen accessSenior author

  Experimentation is inherently difficult because most methods require substantial refinement, calibration, and validation before high-quality, reliable data can be collected. In most cases, experimental outcomes are impacted by multiple variables, thus requiring their simultaneous optimization for single and multi-objective targets. Traditional experimental approaches rely on trial-and-error methods guided by rational decision making, but these become increasingly inefficient and ineffective as complex interactions between inputs limit our ability to capture underlying trends using conventional statistical approaches. Machine learning and active learning (ML/AL) combined with automation represents an approach that can bolster future laboratory productivity. However, a steep initial learning curve and high costs of instrumentation pose substantial barriers to adoption. To democratize access, we herein comprehensively cover both the computational skills and hardware implementation necessary for self-driven experimental workflows. The accompanying open-source, low-cost liquid handling platforms offer practical templates for researchers adopting self-driving lab (SDL) methodologies. Complete tutorials and build guides are provided at https://gormleylab.github.io/SDLGuide.

  PublisherOA PDFDOI

• Phosphorylation toggles the SARS-CoV-2 nucleocapsid protein between two membrane-associated condensate states

  Nature Communications · 2025-08-26 · 8 citations

  articleOpen access

  The Nucleocapsid protein (N) of SARS-CoV-2 plays a critical role in the viral lifecycle by regulating RNA replication and by packaging the viral genome. N and RNA phase separate to form condensates that may be important for these functions. Both functions occur at membrane surfaces, but how N toggles between these two membrane-associated functional states is unclear. Here, we reveal that phosphorylation switches how N condensates interact with membranes, in part by modulating condensate material properties. Our studies also show that phosphorylation alters N's interaction with viral membrane proteins. We gain mechanistic insight through structural analysis and molecular simulations, which suggest phosphorylation induces a conformational change in N that softens condensate material properties. Together, our findings identify membrane association as a key feature of N condensates and provide mechanistic insights into the regulatory role of phosphorylation. Understanding this mechanism suggests potential therapeutic targets for COVID infection.

  PublisherOA PDFDOI

• Automation-Assisted Photoinduced Atom Transfer Radical Polymerization

  ACS Polymers Au · 2025-08-28 · 4 citations

  articleOpen accessSenior authorCorresponding

  Oxygen-tolerant reversible-deactivation radical polymerizations (RDRP) now allow many of these reactions to proceed in open labware, such as well plates. This enables the high-throughput synthesis of tailored polymers and lowers the knowledge barrier required to obtain these materials. Building on our previous work automating photoinduced electron/energy transfer reversible addition-fragmentation chain transfer (PET-RAFT) and enzyme-assisted RAFT (Enz-RAFT) polymerization, we now introduce automated atom transfer radical polymerization (ATRP). Here, we demonstrate the potential of this platform for the high-throughput optimization of ATRP chemistry. Furthermore, we demonstrate that this workflow can help provide insights into the selection of reaction components, such as ligands and initiators, for the polymerization of kinetically difficult monomers such as methyl methacrylate with smaller rates of propagation than acrylates. This coupling paves the way for data-driven optimization of ATRP reactions, accelerated by the generation of high-throughput data sets. To facilitate the integration of robotics for high-throughput applications in polymer synthesis and optimization of photo-ATRP, we have made a Python package available to assist with experimental planning. The tool accepts Excel sheets with user-defined molar ratios, target monomer concentrations, and reagent stock concentrations and outputs an Excel sheet with actionable recipes that can be readily implemented in liquid handling transfer steps via manual or automated pipetting.

  PublisherOA PDFDOI

Recent grants

• Collaborative Research: DMREF: Machine Learning and Robotics for the Data-Driven Design of Protein-polymer Hybrid Materials

  NSF · $1.3M · 2021–2026

• Semi-automated discovery of synthetic polymers with protein features

  NSF · $570k · 2020–2024

Frequent coauthors

• Molly M. Stevens

  Imperial College London

  38 shared

• Robert Chapman

  University of Newcastle Australia

  27 shared

• Joseph A. M. Steele

  NIHR Imperial Biomedical Research Centre

  19 shared

• Matthew Tamasi

  Rutgers, The State University of New Jersey

  16 shared

• Shashank Kosuri

  Rutgers, The State University of New Jersey

  14 shared

• Rachael H. Harrison

  12 shared

• Hamidreza Ghandehari

  University of Utah

  11 shared

• Cyrille Boyer

  UNSW Sydney

  10 shared

Labs

• Gormley LabPI

Education

• Ph.D., Biomedical Engineering

  Rutgers University

• M.S.

  Imperial College London

  2015

• B.S.

  Karolinska Institutet

  2016