Fabian C. Spoendlin

AbSLang-search efficiently retrieves structurally similar antibodies from large sequence databases using a contrastive learning model for CDR RMSD prediction.

ABB4-STEROIDS generates antibody conformational ensembles with state-of-the-art accuracy, outperforming existing methods in experimental benchmarks.

This paper introduces the EDEN family of foundation models for AI-programmable therapeutic design, demonstrating their ability to generate functional gene insertions, antibiotic peptides, and synthetic microbiomes.

This study introduces a molecular dynamics model and databases to analyze CDR loop flexibility in antibodies and TCRs, enabling detailed study of their dynamics and machine learning training.

The paper predicts the structural flexibility of antibody and T-cell receptor CDR3 loops using a new method trained on a large dataset of conformations.

ANARCII is a language model that numbers antigen receptor sequences without alignment, achieving high accuracy and efficiency for diverse sequences.

This study evaluates AF2's ability to predict protein structural ensembles by comparing its results with MD simulations and NMR data for four test proteins.

SPACE2 improves epitope clustering by using structural models, showing higher coverage and diversity in antibodies binding the same epitope compared to sequence-based methods.