**Diff-AMP: Tailored Designed Antimicrobial Peptide Framework with All-in-One Generation, Identification, Prediction and Optimization** Rui Wang, Tao Wang, Linlin Zhuo, Jinhang Wei, Xiangzheng Fu, Quan Zou, and Xiaojun Yao **Corresponding authors:** Linlin Zhuo, E-mail: zhuoninnin@163.com, Xiangzheng Fu, E-mail: fzz326@hnu.edu.cn, Quan Zou, E-mail: zouquan@nclab.net, Xiaojun Yao, E-mail: xjyao@mpu.edu.mo **Abstract:** Antimicrobial peptides (AMPs) are short peptides with diverse functions, effectively targeting and combating various organisms. The widespread misuse of chemical antibiotics has led to increasing microbial resistance. Due to their low drug resistance and toxicity, AMPs are considered promising substitutes for traditional antibiotics. While existing deep learning technology enhances AMP generation, it also presents certain challenges. Firstly, AMP generation overlooks the complex interdependencies among amino acids. Secondly, current models fail to integrate crucial tasks like screening, attribute prediction, and iterative optimization. To address these issues, we develop a comprehensive deep learning framework, Diff-AMP, that automates AMP generation, identification, attribute prediction, and iterative optimization. We innovatively integrate kinetic diffusion and attention mechanisms into the reinforcement learning framework for efficient AMP generation. Additionally, our prediction module incorporates pre-training and transfer learning strategies for precise AMP identification and screening. We employ a convolutional neural network for multi-attribute prediction and a reinforcement learning-based iterative optimization strategy to produce diverse AMPs. This framework automates molecule generation, screening, attribute prediction, and optimization, thereby advancing AMP research. We have also deployed Diff-AMP on a web server, with code, data, and server details available in the Data Availability section. **Keywords:** antimicrobial peptides (AMPs); AMP generation; integrated framework; diffusion; attention mechanisms; reinforcement learning **Introduction:** The rise of multi-drug-resistant superbugs has heightened the urgent need for novel antibacterial drugs. AMPs, known for their low drug resistance and toxicity, have garnered significant attention as potential alternatives to antibiotics. However, designing, screening, and optimizing AMPs face challenges such as high toxicity, sensitivity to extreme environments, specificity issues, folding problems, microbial resistance, and high production costs. Deep learning models that combine peptide sequence data and biophysical assay results have improved the generation of new AMPs. For example, Generative Adversarial Networks (GAN) and Variational Autoencoders (VAE) have made AMP generation more efficient. However, existing models often overlook the stability of the AMP's secondary structure, which is vital for antibacterial activity. **Methods:** We develop Diff-AMP, a comprehensive framework for AMP generation and optimization, combining various deep learning technologies to efficiently accomplish AMP generation, evaluation,**Diff-AMP: Tailored Designed Antimicrobial Peptide Framework with All-in-One Generation, Identification, Prediction and Optimization** Rui Wang, Tao Wang, Linlin Zhuo, Jinhang Wei, Xiangzheng Fu, Quan Zou, and Xiaojun Yao **Corresponding authors:** Linlin Zhuo, E-mail: zhuoninnin@163.com, Xiangzheng Fu, E-mail: fzz326@hnu.edu.cn, Quan Zou, E-mail: zouquan@nclab.net, Xiaojun Yao, E-mail: xjyao@mpu.edu.mo **Abstract:** Antimicrobial peptides (AMPs) are short peptides with diverse functions, effectively targeting and combating various organisms. The widespread misuse of chemical antibiotics has led to increasing microbial resistance. Due to their low drug resistance and toxicity, AMPs are considered promising substitutes for traditional antibiotics. While existing deep learning technology enhances AMP generation, it also presents certain challenges. Firstly, AMP generation overlooks the complex interdependencies among amino acids. Secondly, current models fail to integrate crucial tasks like screening, attribute prediction, and iterative optimization. To address these issues, we develop a comprehensive deep learning framework, Diff-AMP, that automates AMP generation, identification, attribute prediction, and iterative optimization. We innovatively integrate kinetic diffusion and attention mechanisms into the reinforcement learning framework for efficient AMP generation. Additionally, our prediction module incorporates pre-training and transfer learning strategies for precise AMP identification and screening. We employ a convolutional neural network for multi-attribute prediction and a reinforcement learning-based iterative optimization strategy to produce diverse AMPs. This framework automates molecule generation, screening, attribute prediction, and optimization, thereby advancing AMP research. We have also deployed Diff-AMP on a web server, with code, data, and server details available in the Data Availability section. **Keywords:** antimicrobial peptides (AMPs); AMP generation; integrated framework; diffusion; attention mechanisms; reinforcement learning **Introduction:** The rise of multi-drug-resistant superbugs has heightened the urgent need for novel antibacterial drugs. AMPs, known for their low drug resistance and toxicity, have garnered significant attention as potential alternatives to antibiotics. However, designing, screening, and optimizing AMPs face challenges such as high toxicity, sensitivity to extreme environments, specificity issues, folding problems, microbial resistance, and high production costs. Deep learning models that combine peptide sequence data and biophysical assay results have improved the generation of new AMPs. For example, Generative Adversarial Networks (GAN) and Variational Autoencoders (VAE) have made AMP generation more efficient. However, existing models often overlook the stability of the AMP's secondary structure, which is vital for antibacterial activity. **Methods:** We develop Diff-AMP, a comprehensive framework for AMP generation and optimization, combining various deep learning technologies to efficiently accomplish AMP generation, evaluation,