Area of research
Molecular Biology · Materials Chemistry · protein folding alphafold
Research interest
Research interests include Protein Structure and Dynamics, Machine Learning in Bioinformatics, RNA and protein synthesis mechanisms, and Enzyme Structure and Function.
AlphaFold Protein Structure Database 2025: a redesigned interface and updated structural coverage.
On the state of protein function prediction: a report on the fourth CAFA challenge
Metagenomic-scale analysis of the predicted protein structure universe
Metagenomic-scale analysis of the predicted protein structure universe
Foldclass and Merizo-search: scalable structural similarity search for single- and multi-domain proteins using geometric learning.
Exploring structural diversity across the protein universe with The Encyclopedia of Domains.
Exploring structural diversity across the protein universe with The Encyclopedia of Domains
Foldclass and Merizo-search: embedding-based deep learning tools for protein domain segmentation, fold recognition and comparison
Merizo: a rapid and accurate protein domain segmentation method using invariant point attention.
Machine learning methods for predicting protein structure from single sequences
Merizo: a rapid and accurate domain segmentation method using invariant point attention
A guide to machine learning for biologists.
The impact of AlphaFold2 one year on.
Design in the DARK: Learning Deep Generative Models for De Novo Protein Design
Ultrafast end-to-end protein structure prediction enables high-throughput exploration of uncharacterized proteins.
A guide to machine learning for biologists
Critical assessment of protein intrinsic disorder prediction
Increasing the accuracy of single sequence prediction methods using a deep semi-supervised learning framework.
Using AlphaFold for Rapid and Accurate Fixed Backbone Protein Design
Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins
Improved protein structure prediction using potentials from deep learning
Protein function prediction is improved by creating synthetic feature samples with generative adversarial networks
Ultrafast end-to-end protein structure prediction enables high-throughput exploration of uncharacterised proteins
A Deep Semi-Supervised Framework for Accurate Modelling of Orphan Sequences
The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens
Setting the standards for machine learning in biology.
Framework engineering to produce dominant T cell receptors with enhanced antigen-specific function.
Prediction of inter-residue contacts with DeepMetaPSICOV in CASP13
Working toward precision medicine: Predicting phenotypes from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges
An expanded evaluation of protein function prediction methods shows an improvement in accuracy