Dongmin (Eugene) Bang

• Ph.D. Candidate in Bioinformatics at Seoul National University @ Bio & Health Informatics Lab. with Prof. Sun Kim

• Senior Research Scientist @ **AIGENDRUG** Co., Ltd.

• PharmD & Licensed pharmacist in Republic of Korea

  Research Interests:

  • Precision medicine and Multi-modal learning
  • AI for drug discovery
  • Intelligent Knowledge Integration

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.

EDUCATION

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

(Advisor: Sun Kim)

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

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

PUBLICATIONS & PROCEEDINGS

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(: Equal contributions)*

1. D Bang, I Sung, Y Piao, S Lee, S Kim. “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. S Ha*, D Bang*, S Kim. “FATE-Tox: Fragment Attention Transformer for E(3)-Equivariant Multi-Organ Toxicity Prediction.” Journal of Cheminformatics (May, 2025)
5. 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)