{"type":"ScholarlyArticle","title":"Peptide Vaccine Design by Evolutionary Multi-Objective Optimization","authors":[{"name":"Dan-Xuan Liu","slug":"dan-xuan-liu","given":"Dan-Xuan","family":"Liu","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University , Nanjing 210023 , China School of Artificial Intelligence ,"},{"name":"Yi-Heng Xu","slug":"yi-heng-xu","given":"Yi-Heng","family":"Xu","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University , Nanjing 210023 , China School of Artificial Intelligence ,"},{"name":"Chao Qian","slug":"chao-qian","given":"Chao","family":"Qian","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University , Nanjing 210023 , China School of Artificial Intelligence ,; Nanjing University , Nanjing 210023 , China"}],"abstract":"Peptide vaccines are growing in significance for fighting diverse diseases. Machine learning has improved the identification of peptides that can trigger immune responses, and the main challenge of peptide vaccine design now lies in selecting an effective subset of peptides due to the allelic diversity among individuals. Previous works mainly formulated this task as a constrained optimization problem, aiming to maximize the expected number of peptide-Major Histocompatibility Complex (peptide-MHC) bindings across a broad range of populations by selecting a subset of diverse peptides with limited size; and employed a greedy algorithm, whose performance, however, may be limited due to the greedy nature. In this paper, we propose a new framework PVD-EMO based on Evolutionary Multi-objective Optimization, which reformulates Peptide Vaccine Design as a bi-objective optimization problem that maximizes the expected number of peptide-MHC bindings and minimizes the number of selected peptides simultaneously, and employs a Multi-Objective Evolutionary Algorithm (MOEA) to solve it. We also incorporate warm-start and repair strategies into MOEAs to improve efficiency and performance. We prove that the warm-start strategy ensures that PVD-EMO maintains the same worst-case approximation guarantee as the previous greedy algorithm, and meanwhile, the EMO framework can help avoid local optima. Experiments on a peptide vaccine design for COVID-19, caused by the SARS-CoV-2 virus, demonstrate the superiority of PVD-EMO.","doi":"10.24963/ijcai.2024/770","url":"https://ijcai.org/proceedings/2024/770","pdf":"https://ijcai.org/proceedings/2024/0770.pdf","markdown":"https://ijcai.org/proceedings/2024/770.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/770","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Search","track":"Main Track","number":770,"pages":{"first":6967,"last":6975},"keywords":["Search: S: Applications","Multidisciplinary Topics and Applications: MTA: Health and medicine","Search: S: Evolutionary computation"],"videos":[],"references":[{"position":1,"raw":"[Abd-Aziz and Poh, 2022] Noraini Abd-Aziz and Chit Laa Poh. 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