{ "describedBy": "https://emanahmed.org/openapi.json", "type": "publication-list", "count": 4, "publishedCount": 3, "author": { "name": "Eman Ahmed", "orcid": "https://orcid.org/0009-0008-7822-9530", "affiliation": "Rutgers, The State University of New Jersey", "url": "https://emanahmed.org/" }, "items": [ { "id": "mapping-biomaterial-complexity-machine-learning", "type": "publication", "status": "published", "title": "Mapping Biomaterial Complexity by Machine Learning", "authors": [ "Eman Ahmed", "Prajakatta Mulay", "Cesar Ramirez", "Gabriela Tirado-Mansilla", "Eugene Cheong", "Adam J. Gormley" ], "authorPosition": "first", "publicationType": "Review", "year": "2024", "date": "2024-10-11", "venue": "Tissue Engineering Part A", "volume": "30", "issue": "19-20", "pages": "662-680", "doi": "10.1089/ten.tea.2024.0067", "doiUrl": "https://doi.org/10.1089/ten.tea.2024.0067", "pmid": "39135398", "pmcid": "PMC12394815", "openAccessUrl": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12394815/", "citationCount": "25", "keywords": [ "biomaterials", "high-throughput experimentation", "machine learning", "structure-property relationships", "tissue engineering", "data mining" ], "summary": "Biomaterials rarely fail or succeed for one obvious reason. Performance usually comes from a combination of small structural details interacting at once. Testing those combinations one experiment at a time does not scale. This review sets out how high-throughput experimentation paired with machine learning changes the search: run many conditions in parallel, keep every data point including the failures, and train models that map structure to function across the whole space rather than at a few sampled points.", "url": "https://emanahmed.org/publications/mapping-biomaterial-complexity-machine-learning.html", "apiUrl": "https://emanahmed.org/api/publications/mapping-biomaterial-complexity-machine-learning.json", "abstract": "Biomaterials often have subtle properties that ultimately drive their bespoke performance. Given this nuanced structure–function behavior, the standard scientific approach of one experiment at a time or design of experiment methods is largely inefficient for the discovery of complex biomaterials. More recently, high-throughput experimentation coupled with machine learning methods has matured beyond expert users allowing scientists and engineers from diverse backgrounds to access these powerful data science tools. As a result, we now have the opportunity to strategically utilize all available data from high-throughput experiments to train efficacious models and map the structure-function behavior of biomaterials for their discovery. Herein, we discuss this necessary shift to data-driven determination of structure–function properties of biomaterials as we highlight how machine learning is leveraged in identifying physicochemical cues for biomaterials in tissue engineering, gene delivery, drug delivery, protein stabilization, and antifouling materials. We also discuss data-mining approaches that are coupled with machine learning to map biomaterial functions that reduce the load on experimental approaches for faster biomaterial discovery. Ultimately, harnessing the prowess of machine learning will lead to accelerated discovery and development of optimal biomaterial designs." }, { "id": "automation-assisted-photo-atrp", "type": "publication", "status": "published", "title": "Automation-Assisted Photoinduced Atom Transfer Radical Polymerization", "authors": [ "Cesar Ramirez", "Eman Ahmed", "Elena Di Mare", "Maria Pineiro-Goncalves", "Apostolos Maroulis", "Prajakatta Mulay", "D. Christopher Radford", "Adam J. Gormley" ], "authorPosition": "contributing", "publicationType": "Research article", "year": "2026", "date": "2026-02-11", "venue": "ACS Polymers Au", "volume": "6", "issue": "1", "pages": "181-193", "doi": "10.1021/acspolymersau.5c00067", "doiUrl": "https://doi.org/10.1021/acspolymersau.5c00067", "pmid": "41693847", "pmcid": "PMC12903423", "openAccessUrl": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12903423/", "citationCount": "10", "keywords": [ "ATRP", "photo-ATRP", "laboratory automation", "high-throughput polymer synthesis", "oxygen tolerance", "reversible-deactivation radical polymerization" ], "summary": "ATRP is one of the workhorse reactions for making polymers with controlled length and composition, but it has historically needed inert, oxygen-free conditions, which rules out running it in an open well plate on a robot. Oxygen-tolerant chemistry removes that constraint. This paper puts photo-ATRP onto an automated liquid-handling platform and uses it to screen reaction components at a scale that is impractical by hand, including for methyl methacrylate, a monomer that propagates slowly enough to be genuinely awkward to optimise.", "url": "https://emanahmed.org/publications/automation-assisted-photo-atrp.html", "apiUrl": "https://emanahmed.org/api/publications/automation-assisted-photo-atrp.json", "abstract": "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." }, { "id": "saxs-assistant-automated-saxs-analysis", "type": "publication", "status": "published", "title": "SAXS Assistant: Automated SAXS analysis for structural discovery in biologics and polymeric nanoparticles", "authors": [ "Cesar Ramirez", "Elena Di Mare", "James Byrnes", "Eman Ahmed", "Maria Pineiro-Goncalves", "Cristian Lopez", "N. Sanjeeva Murthy", "Adam J. Gormley" ], "authorPosition": "contributing", "publicationType": "Research article", "year": "2025", "date": "2025-09-24", "venue": "Biophysical Journal", "volume": "124", "issue": "21", "pages": "3772-3786", "doi": "10.1016/j.bpj.2025.09.034", "doiUrl": "https://doi.org/10.1016/j.bpj.2025.09.034", "pmid": "40999685", "pmcid": "PMC12677192", "openAccessUrl": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12677192/", "citationCount": "8", "keywords": [ "small-angle X-ray scattering", "SAXS", "machine learning", "radius of gyration", "pair distance distribution function", "polymeric nanoparticles", "BioXTAS RAW" ], "summary": "SAXS tells you the size and shape of something in solution, but getting there involves judgement calls: where to set the Guinier range, whether a P(r) fit is trustworthy, what maximum dimension to believe. Those calls are slow and they vary between analysts, which is a problem once you are producing hundreds of profiles. SAXS Assistant automates the pipeline, trains a model on 1,940 experimental profiles from the SASBDB to estimate Dmax, clusters profiles against known biomolecular shapes, and flags results it is not confident about instead of quietly returning them.", "url": "https://emanahmed.org/publications/saxs-assistant-automated-saxs-analysis.html", "apiUrl": "https://emanahmed.org/api/publications/saxs-assistant-automated-saxs-analysis.json", "abstract": "Small-angle x-ray scattering (SAXS) is a powerful technique for assessing macromolecular structure. High-throughput SAXS is limited by the time-consuming and, at times, subjective nature of SAXS data interpretation. Here, we present SAXS Assistant, a Python-based script that streamlines SAXS data analysis to extract features for machine learning (ML) and key structural parameters, including the Guinier radius of gyration (Rg), pair distance distribution function (PDDF)-derived Rg, maximum particle dimension (Dmax), and Kratky plots. The script builds upon BioXTAS RAW and validates reliability via Guinier/PDDF Rg agreement, an important indicator of well-measured data sets. For assistance in Dmax estimation, a multilayer perceptron regressor was trained with 1940 data files from the Small Angle Scattering Biological Data Bank. The model achieved a test set performance R2 = 0.90 and mean absolute error = 11.7 Å. Training exclusively with experimental data translates analyses from researchers, including experts in the field, to the ML model, which helps assess Dmax estimations from PDDF. Gaussian mixture model clustering was implemented to classify profiles into structural classes based on entries in the Small Angle Scattering Biological Data Bank. Users may therefore assess the similarity between experimental samples and known biomolecular shapes within the mapped repository entries. This probabilistic clustering aids in quantifying information from Kratky and generating shape-descriptive features. SAXS Assistant accelerates SAXS data analysis through enforced quality control, ML-ready outputs, and flags for low-confidence results. In addition to providing the ability to analyze large data sets at high throughput, this tool is versatile and may serve researchers in both biological and synthetic polymer research fields." }, { "type": "publication", "status": "inprep", "title": "High-Throughput Approach for Evaluating the Solubility of Polymer-Stabilized Proteins in Organic Solvent", "authors": [ "Eman Ahmed", "Adam J. Gormley" ], "authorPosition": "first", "publicationType": "Manuscript in preparation", "summary": "The doctoral project: a plate-based, automated assay for asking which random copolymers keep an enzyme soluble and active once it is moved into a water-miscible organic solvent." } ], "license": { "content": "CC-BY-4.0", "contentUrl": "https://creativecommons.org/licenses/by/4.0/", "note": "Site prose and this API's metadata may be reused with attribution to Eman Ahmed (emanahmed.org). Published abstracts are reproduced from the publisher of record named in each record's `doi`; their reuse is governed by that publisher, not by this licence.", "attribution": "Eman Ahmed, https://emanahmed.org" }, "provenance": { "source": "Generated from the site's single source of truth at build time. Bibliographic fields were taken from the publisher of record or PubMed Central, not from memory.", "citationMetricsAsOf": "September 2026", "canonical": "https://emanahmed.org/" } }