AMPidentifier: An Ensemble Machine Learning-Based Toolkit for Antimicrobial Peptide Prediction

This article presents AMPidentifier, an open-source modular Python toolkit that uses ensemble machine learning techniques to predict antimicrobial peptide sequences. Against the backdrop of the antibiotic resistance crisis, this tool provides an efficient computational screening solution for the discovery of new antimicrobial drugs. By combining sequence feature extraction and multi-model ensemble strategies, it helps accelerate the research and development of antimicrobial peptides.

Antibiotic resistance has become one of the top ten global public health threats. Traditional antibiotic research and development have long cycles, high costs, and low success rates, making it difficult to cope with the evolution of resistance. Antimicrobial peptides (AMPs), as components of the innate immune system of organisms, have advantages such as broad-spectrum antibacterial activity, low resistance risk, and immunomodulatory functions, making them an important direction to combat "superbugs". Their mechanism of action is to disrupt bacterial cell membranes, and their sequence features (positive net charge, amphipathicity, proportion of hydrophobic amino acids, etc.) provide a basis for machine learning prediction. However, experimental screening is time-consuming, so computational tools have become crucial.

AMPidentifier adopts a modular design, including modules for data preprocessing, feature extraction, model training, prediction inference, and result visualization. Feature engineering covers sequence composition (amino acid frequency, physicochemical property statistics), physicochemical property encoding (mapping of hydrophobicity scales, etc.), and pre-trained protein language model embeddings. The core is an ensemble learning strategy (voting, stacking, deep learning integration), which improves generalization ability through cross-validation and hyperparameter optimization and handles class imbalance issues. The usage workflow includes FASTA data preparation and cleaning, feature extraction (supporting multi-threading), optional model training, batch prediction, and result analysis (exporting multiple formats, visualization). It supports GPU acceleration and distributed training.

In tests on multiple public benchmark datasets, the ensemble model of the tool outperforms single models in metrics such as accuracy, sensitivity, specificity, MCC, and AUC-ROC, and is competitive compared to existing tools (e.g., iAMPpred, CAMPR3). In terms of interpretability, key sequence features are identified through feature importance analysis, and SHAP values reveal the impact of amino acid positions on predictions, helping to understand model decisions and guide peptide design.

The tool is mainly applied in virtual screening (quickly scoring candidates from large-scale peptide libraries and prioritizing experimental validation), omics data mining (identifying antimicrobial peptide genes from genomes/transcriptomes), and peptide engineering optimization (predicting the impact of mutations on activity and designing variants). It significantly improves the efficiency of antimicrobial peptide discovery and reduces R&D costs.

Current limitations: It does not explicitly consider 3D structural information, training data bias affects generalization, it only supports binary classification (no quantitative indicators such as MIC), and it does not predict druggable properties like toxicity. Future directions: Integrate structure prediction tools (e.g., AlphaFold) to introduce 3D features, use multi-task learning to predict multiple activities, build quantitative structure-activity relationship models, and use generative models to assist de novo antimicrobial peptide design.

AMPidentifier is a typical case of the combination of machine learning and bioinformatics, providing a powerful computational tool to address the challenge of antibiotic resistance. As an open-source project, it promotes collaboration and knowledge sharing, accelerating the translation from basic research to clinical applications. In the era of integration between AI and life sciences, such tools will play a key role in fields like drug discovery.