| 摘要： | Abstract: Humans have long used antibiotics to fight bacteria, but increasing drug resistance has
reduced their effectiveness. Antimicrobial peptides (AMPs) are a promising alternative with natural
broad-spectrum activity against bacteria and viruses. However, their instability and hemolysis limit
their medical use, making the design and improvement of AMPs a key research focus. Designing
antimicrobial peptides with multiple desired properties using machine learning is still challenging,
especially with limited data. This study utilized a multi-objective optimization method, the nondominated sorting genetic algorithm II (NSGA-II), to enhance the physicochemical properties of
peptide sequences and identify those with improved antimicrobial activity. Combining NSGA-II
with neural networks, the approach efficiently identified promising AMP candidates and accurately
predicted their antibacterial effectiveness. This method significantly advances by optimizing factors
like hydrophobicity, instability index, and aliphatic index to improve peptide stability. It offers a
more efficient way to address the limitations of AMPs, paving the way for the development of safer
and more effective antimicrobial treatments. |
