Scholar
Seonghwan Seo
Google Scholar ID: NGc_Z_UAAAAJ
Ph.D. candidate in Chemistry, KAIST
Citations & Impact
Citations
151
H-index
5
i10-index
5
Publications
6
Co-authors
6
list available
Publications
Resume (English only)
Academic Achievements
- Publications: TacoGFN (TMLR 2024), CGFlow (ICML 2025), BBAR (Advanced Science 2023), RxnFlow (ICLR 2025), PharmacoNet (Chemical Science 2024), Unsupervised drug-likeness (Chemical Science 2022); Awards: Spotlight presentation at ICLR 2025 GEM and AI4Mat Workshop; Projects: Hyper Screening X, powered by RxnFlow, has become the world's largest virtual library search with access to eMolecules' 11 trillion compound library.
Research Experience
- Developed deep learning models in various areas of drug discovery including generative modeling, virtual screening, property prediction, and pharmacophore modeling; Recently focused on Generative Flow Networks (GFlowNets), incorporating synthesis-oriented generative modeling to replace traditional in silico virtual screening and in vitro high-throughput screening.
Education
- Degree: Ph.D.; Institution: Korea Advanced Institute of Science and Technology (KAIST); Advisor: Prof. Woo Youn Kim; Time: Current; Major: Chemistry.
Background
- Research Interests: AI-driven scientific discovery, particularly in small molecule drugs; Field: Chemistry; Bio: Ph.D. student in the Department of Chemistry, KAIST, under the supervision of Prof. Woo Youn Kim.
