Author

Jiho Sim

Seoul National University

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

Click to filter author papers

Biochemistry· 1 Bioengineering· 1 Cell Biology· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Approach:deep learning· 3 Research Focus:drug response / drug discovery· 2 Assay:protein structure· 2 Research Focus:cell type annotation· 1 Organism:human· 1 Research Focus:protein folding & dynamics· 1 Assay:proteomics· 1 Approach:statistical model· 1

Frequent Co-Authors

Chaok SeokiD· 2 shared Hyeonuk WooiD· 2 shared Changsoo LeeiD· 1 shared Chulhwan S. KwakiD· 1 shared Daehyeon Gwak· 1 shared Danyeong Lee· 1 shared Deok Hyang Sa· 1 shared Dohoon LeeiD· 1 shared

Activity by Year

2025-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersBioengineering

Chaok SeokORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

MitoAtlas: a Domain-Resolved Spatial Map of the HumanMitochondrial Proteome

Jiwoong Kang, Shin Sh, Chulhwan S. Kwak, Song-Yi Lee, Minkyo Jung, Hanseul Lee, Myeong‐Gyun Kang, Jiho Sim, Seung-Jae V. Lee, Ji Young Mun, Jong‐Seo Kim, Hyun‐Woo Rhee

MitoAtlas maps the sub-mitochondrial localization and topology of 861 proteins using SR-PL and AlphaFold, identifying new interactions and unannotated proteins.

bioRxiv · Apr 5, 2026 · PreprintCell Biology

Precision at Every Scale: Efficiency in AI-Driven De Novo Antibody Design

Hyeonjin Cha, Kyesoo Cho, Jeonghyeon Gu, Daehyeon Gwak, Seung Woo Ham, Mirim Hong, Sehan Kim, Suyeon Kim, Sohee Kwon, Changsoo Lee, Dohoon Lee, Danyeong Lee, Dohoon Lee, Jungsub Lim, Jinsung Noh, , Jinsol Yang

This paper evaluates GaluxDesign, an AI platform, for de novo antibody design, showing a 10.5% binder rate and strong performance on benchmark targets.

bioRxiv · Nov 21, 2025 · PreprintBioengineering

Deep learning molecular interaction motifs from Receptor structure alone

Seeun Kim, Sung Won Oh, Hyeonuk Woo, Jiho Sim, Chaok Seok, Hahnbeom Park

This paper introduces MotifGen, a deep learning model that predicts molecular interaction motifs from receptor structures, enabling peptide binder design and small-molecule binding site prediction.

Journal of Cheminformatics · Jul 30, 2025Peer-reviewedBiochemistry