Yoichi Kurumida

This study characterizes porcine antibodies using sequence and structural data, identifying unique features that may affect CDR-H3 properties and humanization potential.

This study compares different fine-tuning strategies and classical machine learning approaches for protein function prediction using benchmark datasets.

This study uses deep sequencing and machine learning to optimize antibody discovery by analyzing training data and search space design for high-affinity VHHs.

The paper presents a method that improves protein mutational effect prediction with limited experimental data by combining molecular simulation and protein language models.

The paper analyzes a crowdsourced protein design competition, showing participants designed EGFR binders with varying affinities and highlighting challenges in benchmarking and experimental design.