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Antifungal peptides (AFPs) have emerged as a promising alternative to conventional antifungal drugs due to their low toxicity and low propensity for inducing resistance. In this study, we developed a deep learning\u2010based framework called DeepAFP to efficiently identify AFPs. DeepAFP fully leverages and mines composition information, evolutionary information, and physicochemical properties of peptides by employing combined kernels from multiple branches of convolutional neural network with bi\u2010directional long short\u2010term memory layers. In addition, DeepAFP integrates a transfer learning strategy to obtain efficient representations of peptides for improving model performance. DeepAFP demonstrates strong predictive ability on carefully curated datasets, yielding an accuracy of 93.29% and an F1\u2010score of 93.45% on the DeepAFP\u2010Main dataset. The experimental results show that DeepAFP outperforms existing AFP prediction tools, achieving state\u2010of\u2010the\u2010art performance. Finally, we provide a downloadable AFP prediction tool to meet the demands of large\u2010scale prediction and facilitate the usage of our framework by the public or other researchers. Our framework can accurately identify AFPs in a short time without requiring significant human and material resources, and hence can accelerate the development of AFPs as well as contribute to the treatment of fungal infections. Furthermore, our method can provide new perspectives for other biological sequence analysis tasks.<\/jats:p>","DOI":"10.1002\/pro.4758","type":"journal-article","created":{"date-parts":[[2023,8,19]],"date-time":"2023-08-19T07:04:06Z","timestamp":1692428646000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["D<scp>eepAFP<\/scp>: An effective computational framework for identifying antifungal peptides based on deep learning"],"prefix":"10.1002","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4554-6827","authenticated-orcid":false,"given":"Lantian","family":"Yao","sequence":"first","affiliation":[{"name":"Kobilka Institute of Innovative Drug Discovery, School of Medicine The Chinese University of Hong Kong  Shenzhen China"},{"name":"School of Science and Engineering The Chinese University of Hong Kong  Shenzhen 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