Simon P. Kelow

ConformAb designs antibody sequences that maintain CDR canonical conformations, improving binding affinity without target-specific data.

This paper introduces EVFI and DeepEVFI, machine learning methods that infer variant fitness from directed evolution data, identifying tighter binders missed by conventional approaches.

This paper introduces a machine learning framework for therapeutic antibody design that integrates computational methods with experimental validation, resulting in significantly improved binding affinity for multiple targets.

The paper identifies DNA repair and replication defects in early-onset renal cancer patients without known germline variants, suggesting these defects contribute to cancer development and may inform treatment strategies.

This paper introduces a deep learning method for protein design that uses a sequence denoising autoencoder and function predictor to guide sampling towards functional sequences.

The paper analyzes the structure and sequence of the antibody DE loop, revealing its role in antigen binding and its variability in HIV-1 antibodies.