When Retrieval Beats Generation: A Three-Condition Framework for AI-Driven Molecular Linker Design, with PROTAC as a Case Study
TL;DR
This work takes PROTAC linker design as a representative case where data scarcity, multi-constraint satisfaction, and interpretability requirements simultaneously hold, and provides quantitative evidence of distributional mismatch between PROTAC linkers and general small-molecule linkers.
Abstract
: 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.