TACFormer：基于 Transformer 的靶点条件 PROTAC 从头设计生成模型
TACFormer: Transformer-Based Generative Model for Target-Conditioned De Novo PROTAC Design
把 ESM-2 的蛋白表征当条件喂给分子生成模型，一次生成整个 PROTAC 而不是只生成连接子，做药物设计的可以看看这篇。
arXiv 论文提出 TACFormer，一个面向 PROTAC（蛋白降解嵌合体）从头设计的生成模型。该模型用 GPT 风格的分子解码器，以预训练模型 ESM-2 提取的靶蛋白（POI）序列表征作为条件信号，直接生成完整分子。不同于以往将设计简化为连接子生成、固定弹头和 E3 配体的方法，TACFormer 利用蛋白级信息引导全分子生成。实验显示其生成的 PROTAC 具有较高新颖性和化学多样性，且在留出数据集中生成分子会偏向特定 POI 相关的化学邻域。
TACFormer: Transformer-Based Generative Model for Target-Conditioned De Novo PROTAC Design
Targeted protein degradation (TPD) represents a fundamental shift in drug discovery, moving beyond traditional target inhibition toward the event-driven removal of disease-causing proteins. A prominent strategy for TPD is the use of proteolysis-targeting chimeras (PROTACs), heterobifunctional molecules that selectively route a protein of interest (POI) to the ubiquitin-proteasome system for degradation. While PROTAC design still relies heavily on trial-and-error synthesis and experimental testing, deep generative models offer a promising means to explore PROTAC chemical space in silico and generate novel molecules. However, many existing generative methods formulate PROTAC design primarily as a linker-generation problem, with POI-binding (warhead) and E3-ligase-binding components fixed in advance. This restricts exploration of the full PROTAC chemical space and often leaves the POI implicit through the predefined warhead, rather than using protein-level information as an explicit conditioning signal during generation. To address these limitations, we propose TACFormer: a transformer-based generative model for target-aware de novo PROTAC design. The model conditions a GPT-style molecular decoder on pretrained ESM-2 representations of POI sequences, allowing POI-level information to steer full-molecule generation. Our results show that TACFormer generates highly novel and chemically diverse PROTACs while showing consistent POI-dependent shifts in generated chemical space. In particular, molecules generated under specific target conditions preferentially populate chemical neighborhoods associated with POIs in the held-out dataset, while extrapolating to novel chemotypes. By natively integrating biological context into the generative process, TACFormer moves PROTAC design toward a more biology-informed exploration of degrader chemical space.