The article discusses the design and optimization of antimicrobial peptides (AMPs) as a strategy to combat multidrug-resistant bacteria. AMPs, which are naturally produced by multicellular organisms to defend against pathogens, have been modified and optimized through straightforward design and computer-assisted methods to enhance their antimicrobial activity. The authors highlight the importance of relating primary sequence to peptide structure, emphasizing the need for advanced computer-assisted design strategies to develop more potent, cost-effective, and broad-spectrum AMPs. They review the current status of in silico peptide design, focusing on the optimization of direct antimicrobial activity. The article also covers the challenges of bacterial resistance, the mechanisms of AMP action, and the use of chemogenomics and kernel methods in drug discovery. Additionally, it discusses the role of databases in AMP research and the synthetic production of AMPs, including template-based studies, biophysical modeling, and virtual screening. The authors emphasize the importance of accurate prediction of biological activity from the primary amino-acid sequence and the application of machine-learning methods in peptide design. Finally, they explore the potential of evolutionary algorithms and molecular descriptors in guiding the design process.The article discusses the design and optimization of antimicrobial peptides (AMPs) as a strategy to combat multidrug-resistant bacteria. AMPs, which are naturally produced by multicellular organisms to defend against pathogens, have been modified and optimized through straightforward design and computer-assisted methods to enhance their antimicrobial activity. The authors highlight the importance of relating primary sequence to peptide structure, emphasizing the need for advanced computer-assisted design strategies to develop more potent, cost-effective, and broad-spectrum AMPs. They review the current status of in silico peptide design, focusing on the optimization of direct antimicrobial activity. The article also covers the challenges of bacterial resistance, the mechanisms of AMP action, and the use of chemogenomics and kernel methods in drug discovery. Additionally, it discusses the role of databases in AMP research and the synthetic production of AMPs, including template-based studies, biophysical modeling, and virtual screening. The authors emphasize the importance of accurate prediction of biological activity from the primary amino-acid sequence and the application of machine-learning methods in peptide design. Finally, they explore the potential of evolutionary algorithms and molecular descriptors in guiding the design process.