Seonghwan Seo
Korea Advanced Institute of Science and Technology (KAIST)
I am a Ph.D. student in the Department of Chemistry, KAIST, under the supervision of Prof. Woo Youn Kim.
My research area is AI-driven scientific discovery, with a particular focus on small molecule drugs. I have developed deep learning models in various areas of drug discovery including generative modeling, virtual screening, property prediction, and pharmacophore modeling.
Recently, I have focused on generative modeling with Generative Flow Networks (GFlowNets). By incorporating synthesis-oriented generative modeling, I aim to replace traditional in silico virtual screening and in vitro high-throughput screening.
research highlights
Generative modeling
- Generative flow networks (GFlowNets): TacoGFN (TMLR 2024), CGFlow (ICML 2025)
- Synthesis-oriented molecular design: BBAR (Advanced Science 2023), RxnFlow (ICLR 2025)
In silico evaluation
- Protein-based pharmacophore modelings: PharmacoNet (Chemical Science 2024)
- Drug-likeness scoring: Unsupervised drug-likeness (Chemical Science 2022)
news
| May 02, 2025 | 1 paper is accepted to ICML 2025: CGFlow |
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| May 01, 2025 | Hyper Screening X powered by RxnFlow has became a world’s largest virtual library search with access to eMolecules’ 11 trillion compound library (Blog). |
| Apr 01, 2025 | 1 paper is accepted as spotlight paper to ICLR 2025 GEM and AI4Mat Workshop: CGFlow |
| Jan 12, 2025 | 1 paper is accepted to ICLR 2025: RxnFlow |
| Nov 03, 2024 | 1 paper is accepted to Chemical Science: PharmacoNet |