Author

Seonghwan Seo

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Open access Oct 2026

Synthesis-aware generative design in trillion-scale chemical spaces for automated drug discovery

Autonomous drug discovery via generative molecular design is critically bottlenecked by the production of chemically intractable structures. Multi-trillion-scale make-on-demand libraries guarantee synthetic feasibility, but conventional virtual screening cannot efficiently navigate these vast spaces or address multi-pa...

Seonghwan Seo, Yulseung Sung, Sang-Yeon Hwang et al. · bioRxiv · 0 citations

#protein folding Open access Sep 2026

AtlasFold: Protein structure prediction with metagenomic-scale language models

The Atlas model family is presented, an open and trainable system spanning protein language modeling, monomer folding, and protein-complex prediction, and the training code and data, stage checkpoints, and model weights under the MIT License are released to provide a foundation for advancing PLM-based protein structure...

Seonghwan Seo, Hyeongwoo Kim, Seokhyun Moon et al. · bioRxiv · 0 citations

Open access Aug 2026

Elucidating enzyme–substrate specificity through co-folding foundation model

Boltz2ESI is introduced, an end-to-end framework that predicts enzyme–substrate interactions by leveraging structural knowledge learned by a biomolecular foundation model and consistently outperforms state-of-the-art sequence-based and rigid-docking approaches.

Xi-Wei Cheng, Seonghwan Seo, C. Huh et al. · bioRxiv · 0 citations