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Accurate and efficient prediction of PPIs is critical for understanding complex biological systems and for guiding drug discovery. In this study, we propose SimLite\u2010microenvironment\u2010aware protein embedding (MAPE), a lightweight PPI prediction model built upon the VQ\u2010VAE architecture and optimized for both accuracy and computational efficiency. The model introduces a heterogeneous decoder that reduces parameters and floating\u2010point operations without compromising predictive capability. Furthermore, the parameter\u2010free SimAM is integrated into the encoder, enabling more effective extraction of protein sequence and structural features. The classification module is enhanced by incorporating additive attention and residual connections, thereby improving stability and robustness. Extensive experiments conducted on multiple benchmark datasets, including STRING, SHS27k, and SHS148k, demonstrate that SimLite\u2010MAPE consistently outperforms state\u2010of\u2010the\u2010art baseline methods in terms of predictive accuracy, generalization, and efficiency. These results confirm that SimLite\u2010MAPE provides a scalable and practical solution for large\u2010scale PPI prediction.<\/jats:p>","DOI":"10.1155\/int\/1355567","type":"journal-article","created":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T08:51:36Z","timestamp":1781686296000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SimLite\u2010MAPE: A Lightweight and Efficient PPI Prediction Model With SimAM Attention and VQ\u2010VAE Architecture"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-1385-1525","authenticated-orcid":false,"given":"Chencheng","family":"Zhu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1601-5413","authenticated-orcid":false,"given":"Chunling","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2480-4892","authenticated-orcid":false,"given":"Xianglong","family":"Dai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-7360-5948","authenticated-orcid":false,"given":"Min","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,6,17]]},"reference":[{"key":"e_1_2_11_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106526"},{"key":"e_1_2_11_2_2","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab036"},{"key":"e_1_2_11_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.csbj.2022.08.070"},{"key":"e_1_2_11_4_2","unstructured":"WuL. 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