{"type":"ScholarlyArticle","title":"EnzyPGM: Pocket-conditioned Generative Model for Substrate-specific Enzyme Design","authors":[{"name":"Zefeng Lin","slug":"zefeng-lin","given":"Zefeng","family":"Lin","affiliation":""},{"name":"Zhihang Zhang","slug":"zhihang-zhang","given":"Zhihang","family":"Zhang","affiliation":""},{"name":"Weirong Zhu","slug":"weirong-zhu","given":"Weirong","family":"Zhu","affiliation":""},{"name":"Tongchang Han","slug":"tongchang-han","given":"Tongchang","family":"Han","affiliation":""},{"name":"Xianyong Fang","slug":"xianyong-fang","given":"Xianyong","family":"Fang","affiliation":""},{"name":"Tianfan Fu","slug":"tianfan-fu","given":"Tianfan","family":"Fu","affiliation":""},{"name":"Xiaohua Xu","slug":"xiaohua-xu","given":"Xiaohua","family":"Xu","affiliation":""}],"abstract":"Designing enzymes with substrate-binding pockets is a critical challenge in protein engineering, as catalytic activity depends on the precise interaction between pockets and substrates. Currently, generative models dominate functional protein design but cannot model pocket-substrate interactions, which limits enzyme generation with precise catalytic environments. To address this issue, we propose EnzyPGM, a unified framework that jointly generates enzymes and substrate-binding pockets conditioned on functional priors and substrates, with a particular focus on learning accurate pocket–substrate interactions. At its core, EnzyPGM includes two main modules: a Residue-atom Bi-scale Attention (RBA) that jointly models intra-residue dependencies and fine-grained interactions between pocket residues and substrate atoms, and a Residue Function Fusion (RFF) that incorporates enzyme function priors into residue representations. Also, we curate EnzyPock, an enzyme–pocket dataset comprising 84,336 enzyme–substrate pairs across 1,036 four-level enzyme families. Extensive experiments demonstrate that EnzyPGM achieves state-of-the-art performance on EnzyPock. Notably, EnzyPGM reduces the average binding energy by 0.47 kcal/mol over EnzyGen, showing its superior performance on substrate-specific enzyme design. The code is available at https://github.com/John-Lin98/EnzyPGM.","doi":"10.24963/ijcai.2026/622","url":"https://ijcai.org/proceedings/2026/622","pdf":"https://ijcai.org/proceedings/2026/0622.pdf","markdown":"https://ijcai.org/proceedings/2026/622.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/622","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Multidisciplinary Topics and Applications","track":"Main Track","number":622,"pages":{"first":5585,"last":5593},"keywords":["Multidisciplinary Topics and Applications: Bioinformatics","Multidisciplinary Topics and Applications: Life sciences"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}