Research

Drug discovery scenario frequently happens at an incomplete receptor structure context. We study how to select & dock drug compounds into those receptor structures (e.g. AlphaFold models) using Deep Learning techniques.

Related works

• H. Park et al, PNAS 2018.

• H. Park et al, JCTC 2020.

• N. Hiranuma & H. Park et al, Nat Commun. 2020

Funding

• NRF Korea, Starting Grant 우수신진연구 (2022~2024)

Data sparsity is always a bottleneck for applying DL on protein-ligand interactions. Our goal is to learn a general chemistry-aware AI called MotifNet on Protein-protein interface data instead, and to transfer the knowledge to protein-ligand interface systems for virtual screening (VS) and target receptor identification applications.

Related works

• MotifNet, under preparation.

• H. Park et al, JCTC 2020.

• J. Dou & A. Vorobieva et al, Nature 2018.

Peptides are attractive drug molecules. We aim to develop a general Deep Learning framework for peptide drug discovery based on chemical principles (i.e. receptor structure alone). Peptide backbone & sequences are designed to optimally satisfying the predictions from aforementioned MotifNet AI.

This work is a collaboration with SNU Seok lab.

Related works

• MotifNet, under preparation.