Authors
Mingyuan Xu, Chaoming Huang, Li Pang, Qirui Deng, Hao Zhang, Anjie Qiao, Zhiwen Luo, Zhen Wang, Chang-Yu Hsieh, Zhang Zhang, Tie-Gen Chen, Hongming Chen, Jinping Lei
Published in
JACS Au. Volume 6. Issue 7. Pages 4239-4252. Jul 27, 2026. Epub Jul 07, 2026.
Abstract
Proteolysis targeting chimeras (PROTACs) have emerged as a promising technology in degrading disease-related proteins for drug design. Recent deep generative models can accelerate PROTAC design, but the generated molecules are often difficult to synthesize. Here, we develop the SynPROTAC model, which integrates chemical reaction path-driven molecule assembly with reinforcement learning for the design of synthesizable PROTACs together with favorable binding properties. Specifically, the synthesis-constrained generative model employs a Graph Transformer-encoded warhead or E3 ligand as input and autoregressively samples reaction templates and building blocks through transformer-based decoder for PROTAC construction. The comprehensive evaluations indicated that SynPROTAC is capable of generating new PROTACs with feasible synthetic routes and reasonable physicochemical and binding-related properties. We further applied SynPROTAC to design PROTAC molecules degrading bromodomain-containing protein 4 (BRD4), and two selected compounds were successfully synthesized according to the synthetic routes proposed by SynPROTAC. In the following biological experiments, both of them exhibited nanomolar-level degradation activity against BRD4 and potent antiproliferation activity against MV411 tumor cells. These results demonstrate the capability of SynPROTAC to design novel bioactive PROTAC molecules with feasible synthesis routes.
PMID:
42529327
Bibliographic data and abstract were imported from PubMed on 30 Jul 2026.
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