Dongmin (Eugene) Bang

With a dual background in pharmacy and computational biology, I focus on integrating pharmacological insight with machine learning to tackle a core challenge in drug discovery and precision medicine: extracting actionable knowledge from complex, high-dimensional, and often sparse biomedical data.

My research centers on developing knowledge-aware computational frameworks—including patient-specific gene regulatory modeling, graph-based learning, and multi-modal molecular representation—to improve therapeutic prediction, compound prioritization, and drug-likeness evaluation. These efforts prioritize model interpretability, translational generalizability, and the principled incorporation of domain knowledge into data-driven systems.

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✉️ [email protected]

EDUCATION

2021- Ph.D. in Bioinformatics, Seoul National University, Seoul, Korea

2015- Pharm.D., Chung-Ang University, Seoul, Korea

2012- B.S.E. Student in Architectural Engineering, Hanyang University, Seoul, Korea

2010- Sejong Science Highschool

PUBLICATIONS & PROCEEDINGS

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(: Equal contributions)*

  1. D Bang, I Sung, Y Piao, S Lee, S Kim. “BounDr.E: Predicting Drug-likeness via Biomedical Knowledge Alignment and EM-like One-Class Boundary Optimization.” ICML 2025 (Accepted for poster presentation)
  2. D Bang*, J Kim*, H Song, S Kim. “ADME-Drug-Likeness: Enriching Molecular Foundation Models via Pharmacokinetics-Guided Multi-Task Learning for Drug-likeness Prediction.” ISMB/ECCB 2025 (Accepted)
  3. Y Kim*, D Bang*, B Koo, J Yi, C Cho, J Choi, S Kim. “MixingDTA: Improved Drug-Target Affinity Prediction by Extending Mixup with Guilt-By-Association.” ISMB/ECCB 2025 (Accepted)
  4. D Kong*, Y Ha*, HE Yoo*, D Bang, S Kim. “Survey on AI-Drug Discovery with Knowledge Graphs: Data, Algorithm, and Application.” Journal of Computing Science and Engineering (May, 2025)
  5. S Ha*, D Bang*, S Kim. “FATE-Tox: Fragment Attention Transformer for E(3)-Equivariant Multi-Organ Toxicity Prediction.” Journal of Cheminformatics (May, 2025)
  6. I Sung*, SS Lee*, D Bang, J Yi, S Kim, SH Lee. “MDTR: A Knowledge-Guided Interpretable Representation for Quantifying Liver Toxicity at Transcriptomic Level.” Frontiers in Pharmacology (January, 2025)
  7. J Lee*, D Bang*, S Kim. “Residue-Level Multi-View Deep Learning for ATP Binding Site Prediction and Applications in Kinase Inhibitors.” Journal of Chemical Information and Modeling (JCIM) (January, 2025)
  8. C Cho*, S Lee*, D Bang, Y Piao, S Kim. “ChemAP: Predicting Drug Approval with Chemical Structures before Clinical Trial Phase by Leveraging Multi-Modal Embedding Space and Knowledge Distillation.” Scientific Reports (October, 2024)
  9. M Pak, D Bang, I Sung, S Kim, S Lee. “DGDRP: Drug-specific Gene selection for Drug Response Prediction via re-ranking through propagating and learning biological network.” Frontiers in Genetics (September, 2024)
  10. D Bang*, B Koo*, S Kim. “Transfer Learning of Condition-Specific Perturbation in Gene Interactions Improves Drug Response Prediction.” Bioinformatics (June, 2024) - Transferred from ISMB2024
  11. DY Lee, DH Lee, D Bang, S Kim. “DiSCO: Diffusion Schrödinger Bridge for Molecular Conformer Optimization.” AAAI2024 (February, 2024)
  12. J Gu*, D Bang*, J Yi*, S Lee, D Kim, S Kim. “A Model-agnostic Framework to Enhance Knowledge Graph-based Drug Combination Prediction with Drug-Drug Interaction Data and Supervised Contrastive Learning.” Briefings in Bioinformatics (August, 2023)
  13. D Bang, S Lim, S Lee, S Kim. “Biomedical knowledge graph learning for drug repurposing by extending guilt-by-association to multiple layers.” Nature Communications (June, 2023)
  14. J Shin*, Y Piao*, D Bang, S Kim, K Jo. “DRPreter: Interpretable Anticancer Drug Response Prediction Using Knowledge-Guided Graph Neural Networks and Transformer.” International Journal of Molecular Sciences (IJMS) (November, 2022)
  15. D Bang*, J Gu*, J Park, D Jeong, B Koo, J Yi, J Shin, I Jung, S Kim, S Lee. “A Survey on Computational Methods for Investigation on ncRNA-Disease Association through the Mode of Action Perspective.” International Journal of Molecular Sciences (IJMS) (September, 2022)
  16. S Lim*, S Lee*, Y Piao, MG Choi, D Bang, J Gu, S Kim. “On modeling and utilizing chemical compound information with deep learning technologies: A task-oriented approach.” Computational and Structural Biotechnology Journal (CSBJ) (August, 2022)

Pre-prints & Under Review

Conference Appearances

AWARDS AND HONORS

  1. Best Paper Award, BIOINFO 2024, Gyeongju, Korea - “Transfer Learning of Condition-Specific Perturbation in Gene Interactions Improves Drug Response Prediction”
  2. Best Paper Award, BIOINFO 2023, Yeosu, Korea - “Biomedical knowledge graph learning for drug repurposing by extending guilt-by-association to multiple layers”
  3. Best Paper Award in Artificial Intelligence (First among 391), Korea Computer Congress (KCC) 2022, Jeju, Korea - “Leveraging biological knowledge as guide for random walk on heterogenous network for drug repurposing”

GRANTS AND FELLOWSHIPS

[Advanced Medical Researcher Training Support Program]

PATENTS

[KR Patent] APPARATUS AND METHOD FOR MEASURING DRUG-INDUCED HEPATOTOXICITY (I Sung, SS Lee, S Kim, D Bang, J Yi, SH Lee)

[KR Patent] APPARATUS AND METHOD FOR DRUG REPURPOSING (D Bang, S Kim, SS Lee, SH Lee)