Yeonju Jeong, Jae-Mun Choi, Young-Kuk Kim
DATA 2026 Proceedings, 2026.7
Generative AI has rapidly expanded into molecular linker design, with models such as SyntaLinker, DeLinker, DRlinker, and Link-INVENT being widely proposed. Yet these generation-first approaches often fail to translate into experimental validation in real medicinal chemistry workflows. We argue that this gap arises from a paradigm mismatch rather than implementation immaturity. Under three simultaneous conditions (data scarcity, multi-constraint satisfaction, and interpretability requirements), generation-first approaches face the following structural limitations: the synthesizability of generated molecules cannot be reliably guaranteed at the design stage, outputs are disconnected from medicinal chemists’ interpretive language, and sample complexity exceeds what available data can support. We take PROTAC linker design as a representative case where these three conditions simultaneously hold, and provide quantitative evidence of distributional mismatch between PROTAC linkers and general small-molecule linkers. As an alternative, we propose a property-profile-driven library retrieval approach in which physicochemical profiles are predicted based on the design context and candidates are selected from existing libraries accordingly. We further outline a hybrid research agenda integrating retrieval with generation for data-scarce molecular design.
