한빛사논문
- 조회281
Wonho Zhung 1, Hyeongwoo Kim 1 & Woo Youn Kim 1,2,3,*
1Department of Chemistry, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea.
2AI Institute, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea.
3HITS Inc., 124 Teheran-ro, Gangnam-gu, Seoul 06234, Republic of Korea.
*Corresponding author: correspondence to Woo Youn Kim
Abstract
Deep generative modeling has a strong potential to accelerate drug design. However, existing generative models often face challenges in generalization due to limited data, leading to less innovative designs with often unfavorable interactions for unseen target proteins. To address these issues, we propose an interaction-aware 3D molecular generative framework that enables interaction-guided drug design inside target binding pockets. By leveraging universal patterns of protein-ligand interactions as prior knowledge, our model can achieve high generalizability with limited experimental data. Its performance has been comprehensively assessed by analyzing generated ligands for unseen targets in terms of binding pose stability, affinity, geometric patterns, diversity, and novelty. Moreover, the effective design of potential mutant-selective inhibitors demonstrates the applicability of our approach to structure-based drug design.
논문정보
- Research article
- 2024년 03월 (BRIC 등록일 2024-04-12)
- 국내 연구진
- 바이오·의료융합 > 바이오센싱 및 나노바이오물질
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