기본 정보
연구 분야
프로젝트
논문
구성원
preprint|
인용수 0
·2025
Precision at Every Scale: Efficiency in AI-Driven De Novo Antibody Design
Hyeonjin Cha, Kyesoo Cho, Jeonghyeon Gu, Daehyeon Gwak, S. W. Ham, Mirim Hong, Sohee Kwon, Changsoo Lee, Da Lee, D. Lee, Dohoon Lee, Jungsub Lim, Jinsung Noh, Soyeon Oh, Eunhwi Park, Seongchan Park, Taeyong Park, Eunwoo Ryu, Seongok Ryu, Deok Hyang, Chaok Seok, Moo Young Song, Jonghun Won, Hyeonuk Woo, Jinsol Yang
bioRxiv (Cold Spring Harbor Laboratory)
초록

Abstract The precise de novo design of antibodies remains a therapeutic challenge. The AI platform, GaluxDesign, validated its capabilities through two strategies: prior work showed comprehensive exploration, and this study validates a high-efficiency, precision approach. GaluxDesign was applied to eight distinct epitopes across six therapeutic targets, synthesizing and testing a focused set of 50 de novo IgG candidates per epitope. This precision-scale campaign yielded a 10.5% binder rate (estimated EC 50 < 100 nM), identifying target-specific binders for seven of eight epitopes. These novel antibodies exhibit therapeutically relevant properties, with sub-nanomolar to single-digit nanomolar dissociation constants (K d ) confirmed for multiple candidates. These findings confirm that a high-efficiency, precision-scale workflow is a viable approach for generating novel, high-affinity therapeutic antibodies.

키워드
AntibodyWorkflowEpitopeSet (abstract data type)Dissociation rate
타입
preprint
IF / 인용수
- / 0
게재 연도
2025

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