近年來，COVID-19疫情對全球生活帶來重大影響，迫切需要發展有效的治療方法。儘管自2020年底以來已推出多種COVID-19疫苗和治療藥物，但對某些患者而言，接種疫苗和使用現有藥物仍存在一定風險，因此仍需要開發對人體更為安全的治療方式。
基於肽的治療方法近年來受到廣泛關注，因其具有低毒性和高目標選擇性，被視為有潛力的治療途徑。許多抗病毒肽 (Antiviral Peptides, AVPs) 已被證實具有抗病毒活性，適用於預防或治療病毒性疾病，其中能對抗冠狀病毒的肽稱為抗冠狀病毒肽。抗冠狀病毒肽 (Anti-coronavirus Peptide, ACVP) 被視為治療冠狀病毒感染的有力候選藥物。然而，傳統的生物實驗驗證具有抗冠狀病毒活性的胜肽耗時且成本高昂。為解決此問題，許多研究已開始運用傳統機器學習或深度學習方法，以預測可能具有抗冠狀病毒活性的潛在胜肽。
本研究提出了一個模型來預測抗冠狀病毒肽。所提出的模型採用了對比學習的概念來編碼蛋白質序列的特徵。這些編碼後的序列隨後被輸入至一個隨機森林模型進行預測。本研究進行的實驗結果顯示，所提出的模型在iACVP資料集上展現出優異的表現，其曲線下面積達到0.936。

In recent years, the COVID-19 pandemic has significantly impacted global life, urging the development of effective treatments. Despite the introduction of various COVID-19 vaccines and therapeutics since late 2020, there remain risks associated with vaccine administration and existing drugs for certain patients, highlighting the need for safer treatment approaches.

Peptide-based therapies have garnered attention due to their low toxicity and high target specificity, presenting a promising avenue for treatment. Many antiviral peptides (AVPs) have been proven effective against viral diseases, including coronaviruses. Among these AVPs, peptides with activity against coronaviruses are known as anti-coronavirus peptides (ACVPs), holding potential as candidates for treating coronavirus infections. However, traditional biological experiments to validate ACVP activity are time-consuming and costly. To address this, numerous studies have utilized traditional machine learning and deep learning methods to predict potential ACVPs.

This study proposes a model to predict ACVPs. The proposed model adopts the concept of contrastive learning to encode protein sequences’ features. The encoded sequences are then fed into a random forest model for prediction. The experimental results conducted in this study demonstrate the excellent performance of the proposed model on the iACVP dataset, achieving an AUC of 0.936.

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