Experience

ModelMole

Co-founder / CSO

March 2025 - Present

I transitioned to full-time at ModelMole in March 2025, having co-founded the company in 2023, to lead the scientific development of the company. In this short time I have built up our discovery pipeline, enabling two new drugs to reach animal trials in under 12 months. My work focuses on refining our AI-driven computational platform, which integrates physics-backed simulations and molecular modelling to streamline the journey from idea to lab-ready discovery.

City University of New York - Advanced Science Research Center

Knowledge Exchange

September 2024

Knowledge exchange program and writing a €10 mil synergy grant application.

University of Strathclyde

Research Associate

October 2022 - March 2025

  • Developing machine learning and molecular dynamics techniques to accelerate the discovery of (among other things): novel pore-forming peptides, anti-cancer peptides, directional apatite (teeth) regrowth.
  • Deep learning neural network methods to screen catalytic chemical space for new catalysts with enhanced activity and to elucidate their associated reaction mechanisms.
  • Supervising MChem and PhD students.

Siemens Healthineers

Internship

July 2017 - August 2017

Writing programs to verify the accuracy of blood test kit batches to different national standards.

H2-ecO

Coordinator of IT

August 2014 - August 2015

Installing solar panels on homes, maintaining customer databases, general IT support.

Awards

  • ▪Early Career Invited Presentation Award (Materials Research Society, 2023, San Francisco)
  • ▪Best Research Poster Award (Peptide Self-Assembly Conference, 2023, Manchester)

Highlighted Publications

De novo design of alpha-helical peptide amphiphiles repairing fragmented collagen type I via supramolecular co-assembly. Phys. Chem. Chem. Phys. 2025, doi: 10.1039/D4CP01404A

An active machine learning discovery platform for membrane-disrupting and pore-forming peptides. arXiv. 2024, doi: arXiv:2507.14577

Integrating computation, experiment, and machine learning in the design of peptide-based supramolecular materials and systems. Angew. Chem. Int. Ed. 2023, doi: 10.1002/anie.202218067

Constant pH Coarse-Grained Molecular Dynamics with Stochastic Charge Neutralization. J. Phys. Chem. Lett. 2022, doi: 10.1021/acs.jpclett.2c00544

Beyond Tripeptides: Two-Step Active Machine Learning for Very Large Data Sets. J. Chem. Theory Comput. 2021, doi: 10.1021/acs.jctc.1c00159