Intern computational structure analysis using MD, AI​/ML HDX-MS

Position: Intern for computational structure analysis using MD, AI/ML with HDX-MS
Location: Spring House

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At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and Med Tech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity. Learn

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Career Programs

Job Sub     Function:
Non-LDP Intern/Co-Op

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Career Program

All Job Posting Locations:
Spring House, Pennsylvania, United States of America

Job Description:

About Innovative Medicine    Our expertise in Innovative Medicine is informed and inspired by patients, whose insights fuel our science-based advancements. Visionaries like you work on teams that save lives by developing the medicines of tomorrow.
Join us in developing treatments, finding cures, and pioneering the path from lab to life while championing patients every step of the way. Learn more at
We are searching for the best talent for on  Computational Characterization of Protein Structures using MD and AI/ML with HDX-MS Intern  to be in  Spring   House, PA.
The  Intern  term is from June to August, 2026.
Full time requirement (40 hours per week).

Purpose:

Accurate prediction of protein and protein complex structures is essential throughout structure-based drug discovery. Recent AI/ML advances significantly improved prediction accuracy and efficiency, particularly diffusion-based co-folding methods like Alpha

Fold3, Boltz-2, Chai-2, RoseTTAFold All-Atom, and Open Fold-3, now enable reliable in silico predictions of experimentally validated structures and key binding sites for diverse biomolecular complexes including protein-small molecule, protein-PROTAC/molecular glue ternary complexes, protein-peptide, protein-nucleic acid, and antibody-antigen complexes. These tools generate both static structures and conformational ensembles, facilitating integration with molecular dynamics simulations to capture system dynamics in understanding the mechanism of action (MOA).

Despite their promising utility, these predictions still require experimental confirmation (e.g., X-ray or Cryo-EM), which often relegates their application to retrospective studies and limits their impact in early-phase drug discovery.
Hydrogen-deuterium exchange mass spectrometry (HDX-MS) is increasingly used to study protein-protein and protein-ligand interactions by measuring the exchange rate of backbone amide hydrogens with deuterium, reflecting flexibility and solvent exposure. HDX-MS offers insights into protein structure and dynamics, complementing high-resolution X-ray data. It is easier and earlier to use than X-ray or Cryo-EM in early drug discovery. Recently, HDX-MS has seen broader application in physics-based simulations, and AI/ML models now reconstruct HDX data and help prioritize protein structure models by matching experimental results to structures.
Co-folding models and HDX-MS are widely used at Johnson & Johnson, driving both large and small molecule modalities. Multiple co-folding platforms have been established, and extensive HDX-MS spectra for various protein targets, along with physics-based computational analysis tools, have been developed. By further integrating AI/ML capabilities and evaluating physics- and ML-based HDX methods for prioritizing co-folding models, structure-based design can be improved before X-ray or Cryo-EM data are available.

This approach will accelerate our drug discovery cycle and enhances collaboration with internal and external partners.
The main objective of this project aims to integrate public HDX modeling methods with internal physics-based approaches in the JNJ platform to improve selection of correct bound complex structures, reweight MOA-related conformational ensembles, and identify potential cryptic…