2026· International Conference on Data Technologies and Applications· pp. 1188-1195· 0 citations· 13 references
Computer Science
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.
Structure-based drug design (SBDD) models are central to modern pharmaceutical research, enabling the rational exploration of protein-ligand interactions at atomic resolution. However, most existing approaches frame molecular generation as an isolated optimization or a one-to-one matching task, overlooking the shared binding patterns and intrinsic similarities among protein-ligand complexes. This fragmented perspective constrains their ability to capture the fundamental principles governing molecular recognition and binding specificity. Moreover, the limited availability of high-quality experimental data further hampers model generalization and real-world applicability. To address these challenges, we present READ, a retrieval-alignment molecular generation framework that conditions the generative process on small molecules targeting homologous proteins. Retrieved ligands are aligned with a diffusion model across multiple representational spaces and integrated as conditional guidance throughout successive stages of generation. Under a standardized docking-based evaluation protocol, READ achieves consistently strong performance against state-of-the-art SBDD methods. More importantly, it introduces a retrieval-alignment paradigm for structure-based molecular generation, offering a practical framework for early-stage computational hit generation while leaving prospective experimental validation as future work.
Dong Xu, Zhangfan Yang, Junchuang Cai et al.· IEEE transactions on computa...· 1 citation
The results suggest that coupling generation with executable verification and feedback-guided refinement is an effective way to improve text-to-molecule generation.
Qian Tan, Xuanyu Zhu, Lei Jiang et al.· arXiv.org· 0 citations
This is the first method to expose GNN-derived attributions to an LLM as evidence for property prediction, and achieves the best overall results among generalist models and narrows the gap to specialist models tuned for each task.
Junwoo Park, Minyoung Shin, C. Lee et al.· 0 citations
RetFold is proposed, a zero-training baseline that constructs backbones by retrieving CATH domains and refining inter-domain connections through geometry-based helix-linker optimization, at two orders of magnitude lower cost on CPU alone.
Tongyu Xu, Yijie Zhang, Mutian He et al.· 0 citations
Structure-based drug design (SBDD) requires ligands that satisfy both 3D target affinity and 1D chemical validity. Existing controllable generation methods often rely on task-specific fine-tuning or externally imposed sampling-time guidance, adding cost and potentially conflicting with evolving 3D geometric constraints. We propose LiFT, a language-informed cross-modal framework built on Flow Matching for trend-guided 3D molecular generation across both de novo design and scaffold hopping. LiFT uses a"Sense-Evolve-Assemble"agent to generate target-aware SMILES as intermediate chemical conditions, from which a pre-trained chemical foundation model extracts continuous semantic priors. These priors are integrated into geometric generation through a lightweight semantic projector with zero-initialized adaptive normalization for stable cross-modal conditioning. We further introduce a Self-Conditioned Decoupled Router (SCDR), which modulates the velocity field according to intermediate structural states during ODE integration. Experiments on Cross-Docked2020 show that LiFT achieves competitive distribution matching while improving medicinal chemistry metrics and maintaining competitive structural validity under task-steering settings without additional generator fine-tuning. Our results suggest that language-derived chemical priors provide effective trend-level guidance for 3D molecular generation. Code and released artifacts are available at https://github.com/kasurl/LiFT.
Tian-Yu Gao, Zhi-Kai Su, Jia-Shu Li et al.· 0 citations
A clear pattern is revealed in LLM spatial capabilities: while they still lag behind state-of-the-art approaches, they are promising and can handle multiple spatial constraints simultaneously, enabling scaling to heterogeneous setups.
Thomas MacDougall, Maksim Kuznetsov, Roman Schutski et al.· arXiv.org· 1 citation
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