Charlie Harris (Charles)

University of Cambridge PhD Student @ University of Cambridge

Hi there! My name is Charlie, a PhD student at Cambridge working on AI for drug design under the supervision of Prof Sir Tom Blundell and Prof Pietro Lio. My background is in Biochemistry, with a strong focus on applying AI to tackle challenges in structural biology, drug discovery, and biotech, particularly through generative modelling and Geometric Deep Learning. I’m also deeply interested in entrepreneurship, science policy, and flying.

Google Scholar GitHub Twitter CV
Education
  • University of Cambridge

    University of Cambridge

    PhD in Computer Science Oct. 2021 - Present

  • Imperial College London

    Imperial College London

    MSc in Bioinformatics and Theoretical Systems Biology Oct. 2020 - Sep. 2021

  • Imperial College London

    Imperial College London

    BSc in Biochemisty Oct. 2017 - Jun. 2020

Experience
  • IQ Capital

    IQ Capital

    Venture Fellow June 2024 - Oct. 2024

  • BenevolentAI

    BenevolentAI

    Machine Learning Research Intern July 2022 - Oct. 2022

Honors & Awards
  • UK-Italy Visiting Researcher Fellowship - Alan Turing Institute 2024
  • Polaris Fellowship - Entrepreneur First 2024
  • Cambridge Half Blue (for gliding) - Univesity of Cambridge 2023
  • CCAIM PhD Studentship - University of Cambridge 2021
  • Associateship - Royal College of Science 2020
  • Gold - UK Chemistry Olympiad 2017
Media
  • UK Foundation for Science and Technology Podcast 2024
  • YouTube Video on AlphaFold 2024
  • iGEM Synthetic Biology Podcast 2021
Selected Publications (view all )
PoseCheck: Generative Models for 3D Structure-based Drug Design Produce Unrealistic Poses
PoseCheck: Generative Models for 3D Structure-based Drug Design Produce Unrealistic Poses

Charles Harris, Kieran Didi, Arian Jamasb, Chaitanya Joshi, Simon Mathis, Pietro Liò, Tom Blundell

MLSB Workshop @ NeurIPS 2023 Spotlight

This work introduced PoseCheck, an extensive analysis of multiple state-of-the-art methods and find that generated molecules have significantly more physical violations and fewer key interactions compared to baselines, calling into question the implicit assumption that providing rich 3D structure information improves molecule complementarity. We make recommendations for future research tackling identified failure modes and hope our benchmark will serve as a springboard for future SBDD generative modelling work to have a real-world impact.

[Paper] [Code]

PoseCheck: Generative Models for 3D Structure-based Drug Design Produce Unrealistic Poses
PoseCheck: Generative Models for 3D Structure-based Drug Design Produce Unrealistic Poses

Charles Harris, Kieran Didi, Arian Jamasb, Chaitanya Joshi, Simon Mathis, Pietro Liò, Tom Blundell

MLSB Workshop @ NeurIPS 2023 Spotlight

This work introduced PoseCheck, an extensive analysis of multiple state-of-the-art methods and find that generated molecules have significantly more physical violations and fewer key interactions compared to baselines, calling into question the implicit assumption that providing rich 3D structure information improves molecule complementarity. We make recommendations for future research tackling identified failure modes and hope our benchmark will serve as a springboard for future SBDD generative modelling work to have a real-world impact.

[Paper] [Code]

DiffSBDD: Structure-based Drug Design with Equivariant Diffusion Models
DiffSBDD: Structure-based Drug Design with Equivariant Diffusion Models

Arne Schneuing*, Charles Harris*, Yuanqi Du*, Arian Jamasb, Ilia Igashov, Weitao Du, Tom Blundell, Pietro Lió, Carla Gomes, Max Welling, Michael Bronstein, Bruno Correia (* equal contribution)

Accepted at Nature Computational Science (not yet in print) 2024

DiffSBDD was one of the first equivariant diffusion models for structure-based drug design.

[Paper] [Code]

DiffSBDD: Structure-based Drug Design with Equivariant Diffusion Models
DiffSBDD: Structure-based Drug Design with Equivariant Diffusion Models

Arne Schneuing*, Charles Harris*, Yuanqi Du*, Arian Jamasb, Ilia Igashov, Weitao Du, Tom Blundell, Pietro Lió, Carla Gomes, Max Welling, Michael Bronstein, Bruno Correia (* equal contribution)

Accepted at Nature Computational Science (not yet in print) 2024

DiffSBDD was one of the first equivariant diffusion models for structure-based drug design.

[Paper] [Code]

All publications