I am currently an Assistant Professor of Department of Computer Science and Artificial Intelligence at Dongguk University, Korea. I received a PhD degree in Bioinformatics at Seoul National University (advisor: Prof. Sun Kim) in 2019, MS degree in Analytical Chemistry at Yonsei University (advisor: Prof. Myeong Hee Moon) in 2013, and BS degree in Chemistry & Applied Statistics (double major) at Yonsei University. My current research focuses on AI based Drug Discovery and developing deep learning methods and data mining approaches for integrating multi-omics data for disease characterization.

<aside> 👋🏻 Contact: sslim at dgu.ac.kr

</aside>

Education | Professional Experiences | Publications | Awards |

Research Interests | Funded Projects | Service and Memberships | Invited Talks

🎓 Education

• PhD: Bioinformatics, Seoul National University (‘19)
  • Thesis Title: Quantification of pathway activity using RNA-seq data
  • Academic Advisor: Professor Sun Kim
• MS: Analytical Chemistry, Yonsei University (‘13)
  • Thesis Title: 인지질의 구조분석을 위한 계산 알고리즘의 개발 및 뇨시료 중 전립선암의 인지질 표지물질 발굴에 관한 연구
  • Academic Advisor: Professor Myeong Hee Moon
• BS: Chemistry, Applied Statistics (Double major), Yonsei University (‘11)
• Daeil Foreign Language High School (French major)

🏛️ Professional Experiences

• Assistant Professor, Dept of Computer Science and Artificial Intelligence, Dongguk University (’23.3 - current)
• Visiting Scholar; Global AI Drug Discovery Center, Ewha Womans University (’22.5 - ‘23.2)
• Postdoc; Bioinformatics Institute, Seoul National University (’19.9 - ‘23.2)

📔 Publications

• Full list of scientific publications → Google Scholar

• Selected recent works

  Year	Venue	Work
  2026	Bioinformatics (ISMB 2026)	DiSPA — Differential substructure-pathway attention for drug response prediction
  2026	Briefings in Bioinformatics	PathCLAST — Pathway-augmented contrastive learning for interpretable spatial transcriptomics
  2026	arXiv	MARBLE — Multi-agent reasoning for bioinformatics learning and evolution
  2023	Nature Communications	DREAMwalk — Knowledge graph learning for drug repurposing across multiple layers
  2022	Cell Metabolism	Anti-senescence activity of adipocytes via the SREBP1c–PARP1 axis
  2021	PNAS	DNMT1 as an epigenetic safeguard of mitochondrial dynamics in adipocytes

• Selected earlier works

  • Lim S, Kim Y, Gu J, Lee S, Shin W, Kim S# (2023) — “Supervised chemical graph mining improves drug-induced liver injury prediction.” iScience
  • Lim S*, Lee S*, Piao Y et al., Kim S# (2022) — “On modeling and utilizing chemical compound information with deep learning technologies: a task-oriented approach.” Computational and Structural Biotechnology Journal
  • Lim S*, Lu Y* et al., Kim S# (2021) — “A review on compound-protein interaction prediction methods: data, format, representation and model.” Computational and Structural Biotechnology Journal
  • Lee S, Lim S, Lee T, Sung I, Kim S# (2020) — “Cancer subtype classification and modeling by pathway attention and propagation.” Bioinformatics
  • Lim S, Lee S, Jung I, Rhee S, Kim S# (2020) — “Comprehensive and critical evaluation of individualized pathway activity measurement tools on pan-cancer data.” Briefings in Bioinformatics
  • Park Y, Lim S, Nam JW, Kim S# (2016) — “Measuring intratumor heterogeneity by network entropy using RNA-seq data.” Scientific Reports
  • Lim S, Byeon SK, Lee JY, Moon MH (2012) — “Computational approach to structural identification of phospholipids using raw mass spectra from nanoflow LC–ESI–MS/MS.” Journal of Mass Spectrometry