报告时间：2025年8月21号 15：30-16：30

报告地点：统计与数据科学学院106

报告题目：AMPGP: Discovering highly effective antimicrobial peptides via deep learning

报告摘要：Antimicrobial peptides (AMPs) have emerged as vital candidates in the fight against antibiotic resistance. The traditional processes for AMPs design and discovery are often time-consuming and inefficient. Here, we propose AMPGP model, which employs deep learning algorithms for both generation and prediction. The generation model incorporates an attention mechanism into the seqGAN framework to generate high-quality AMPs. The prediction model is structured into four distinct feature channels to address the limitations of relying on a single source of information. The evaluation on the independent test set achieved an accuracy of 98.46%, surpassing several advanced models. Ultimately, we identified ten candidated AMPs and the experiment indicated that peptide No. 1 (LITHLFRFKNSGRILM) and No. 2 (FKLSVLYLGRGNIMKAYYGIKIARAG) exhibited broad-spectrum antibacterial and cellular viability, with no significant hemolytic activity observed. The AMPGP model thus presents a promising approach for discovering effective peptides and enhances the potential for clinical applications.

报告人简介：赵世舜，吉林大学数学学院教授，博士生导师。于吉林大学获得博士学位，师从史宁中教授。于2013年-2014年在美国密苏里大学做访问学者。近年来一直从事生存分析、多元统计以及生物医学统计等研究。在国内外名杂志已发表论文SCI论文30余篇。作为项目负责人主持国家自然科学面上项目2项，教育部科研项目1项，省自然科学基金2项。作为主要参加人参加国家自然科学基金项目3项。