Seonghwan Seo

This paper introduces K-Fold, a method that improves prediction of conformational changes in protein complexes by modeling structural transitions from unbound to bound states.

This paper introduces a generative AI framework for drug discovery that efficiently navigates large chemical spaces while ensuring synthetic feasibility and functional success.

The paper introduces AtlasFold, a protein structure prediction method using metagenomic-scale language models, achieving state-of-the-art accuracy and efficient inference.

This paper introduces Boltz2ESI, a framework predicting enzyme-substrate interactions using a foundation model, outperforming existing methods by capturing active-site plasticity and evolutionary context.