Aug 2026· Biotechnology Advances· pp.
109002
· 0 citations· 123 references
Medicine
TL;DR
How recent advances in machine learning are reshaping AMP research is examined, driving a shift from large-scale discovery toward precision-guided prediction and design and emphasizing integrated generative-predictive pipelines, interpretable models, and closed-loop experimental validation as key enablers for the development of potent, selective, and clinically viable antimicrobial therapeutics.
Abstract
The accelerating global crisis of antibiotic resistance demands new therapeutic paradigms, and antimicrobial peptides (AMPs) have emerged as promising candidates owing to their broad activity and reduced propensity for resistance development. However, despite rapid progress in AMP discovery and generation, the accurate prediction of antimicrobial potency and activity spectrum remains a major bottleneck for clinical translation. In this Review, we examine how recent advances in machine learning are reshaping AMP research, driving a shift from large-scale discovery toward precision-guided prediction and design. We first summarize the molecular mechanisms underlying AMP function and critically assess existing AMP databases from the perspective of machine learning readiness, highlighting limitations in quantitative and spectrum-resolved annotations. We then review recent developments in peptide representation learning, describing how modern models encode sequence, structure, and dynamic features to capture antimicrobial activity. Building on this foundation, we discuss progress in de novo AMP design and emerging frameworks for quantitative minimum inhibitory concentration prediction and strain-specific spectrum profiling. Finally, we outline future directions for the field, emphasizing integrated generative-predictive pipelines, interpretable models, and closed-loop experimental validation as key enablers for the development of potent, selective, and clinically viable antimicrobial therapeutics.
A snapshot of AI-driven technologies for AMP design is provided and two modes of AI-driven technologies for AMP design are surveyed, one concentrated on identifying whether current data possess antimicrobial activity and the other on generating AMP candidates with potential therapeutic properties (generation-oriented).
Yong-Qiang Liu, Jie Hu, Ning Zhang et al.· Synthetic and Systems Biotec...· 0 citations
Antimicrobial resistance poses a significant global threat to healthcare systems worldwide. The high cost and short lifespan of new antibiotics due to the rapid evolution of resistance exacerbate this crisis, prompting exploration of diverse strategies to combat AMR. Within the context of AMR diagnostics, machine learning approaches for predicting bacterial resistance are under active investigation. This paper reviews state-of-the-art methods for predicting antibiotic resistance in individual bacterial isolates. The review encompasses both traditional ML algorithms, such as logistic regression and random forests, and more advanced techniques including convolutional and graph neural networks, as well as transformer language models. The inherent high variability of bacterial genomes and the heterogeneity of mutational processes hinder model generalizability, while variations in clinical conditions during strain development contribute to prediction instability on new datasets. This necessitates further research into improved data collection and preprocessing methods, as well as a more comprehensive consideration of both known and emerging resistance mechanisms.
Mikhail Yu. Kuzmenkov, A. G. Vinogradova, A. Y. Kuzmenkov· Clinical Microbiology and An...· 0 citations
With the worsening crisis of antimicrobial resistance, many researchers are now exploring new types of antibacterial agents, and antimicrobial peptides (AMPs) have begun to attract much attention. However, the experimental identification of AMPs in the large space of natural and synthetic sequences is still slow, expensive and labour-intensive. AMP-Transformer is a two-stage deep learning framework that combines self-supervised pre-training on large-scale unlabelled protein corpora with supervised fine-tuning on curated AMP datasets in this study. The model is a multi-layer bidirectional Transformer encoder that learns contextual residue representations via masked residue modelling, and is then fine-tuned for two coupled tasks: binary discrimination of AMPs from non-AMPs and regression of minimum inhibitory concentration (MIC) values. We used the benchmark datasets built by DBAASP v3, DRAMP 4.0 and other recently published experimental collections for evaluation. On an independent test set, AMP-Transformer had an accuracy of 95.3%, a Matthews correlation coefficient of 0.906, and an area under the receiver operating characteristic curve (AUC-ROC) of 0.986, and outperformed the support vector machine, random forest, convolutional, recurrent and hybrid baselines significantly. Ablation studies show that self-supervised pre-training and multi-head self-attention are the two main contributors, accounting for about 4% of the accuracy increase. Analysis of the learned attention maps shows that the model has independently learned the amphipathic periodicity and cationic residue enrichment characteristic of membrane-active peptides, thereby providing a degree of mechanistic interpretability that is rare among black-box predictors. A subsequent screening of metagenomic open reading frames also produced a ranked list of candidate AMPs with low sequence identity to any training examples, demonstrating the value of the framework for early-stage discovery. Based on the above results, Transformer-based protein language models are relatively stable, interpretable and scalable paradigms for AMP discovery and activity prediction, and they can help establish a practical computational pipeline that selects promising peptide candidates for experimental validation at a lower cost compared with traditional methods.
Shuwen Pan, Eason Soo, Konken Wong· International journal of com...· 0 citations
Motivation Antimicrobial resistance (AMR) has been identified as a top global public health threat. Accurate AMR phenotype prediction from whole-genome sequencing data is an essential tool for accelerating clinical decision-making and mitigating resistance spread. Although many previous works have explored the use of tree-based machine learning (ML) models to predict resistance, the field lacks a systematic evaluation of the training pipeline across a variety of pathogenic species and antibiotics. Results Using nine clinically relevant species–antibiotic combinations from the NCBI antimicrobial susceptibility testing database, we present a detailed analysis of the ML pipeline and identify key factors affecting model performance and evaluation. We begin by relabelling all isolates using current CLSI minimum inhibitory concentration breakpoints to resolve inconsistencies and increase available data, resulting in up to a 19% label swap and 56% data enlargement per species– antibiotic combination. We identify several key training parameters including k-mer length, which can increase classification F1 scores by over 20 points compared to commonly used k-values, feature matrix truncation, which can induce polynomial time reductions with limited performance reduction, and ML model class. By comparing 5-fold cross-validation with evaluation on an unseen clinical dataset, we show that random cross-validation splits—often criticized as overly optimistic—can act as a strong proxy for downstream clinical performance, yielding closer F1 scores than phylogeny-aware splits in all cases. We finally present an interpretability study which shows that over 95% of k-mers used by our models are associated with identifiable genomic features. Our results highlight the importance of feature design, evaluation protocol, and biological analysis in genomic AMR prediction, and support tree-based models as a robust and interpretable method. Availability and implementation Python code is made freely available: https://github.com/chandar-lab/amr-pred
Alex Aselstyne, E. Karthik, Meriem El Azami et al.· bioRxiv· 0 citations
It is concluded that computational protein structure prediction plays a critical role in accelerating antibiotic drug discovery and offers substantial potential for addressing antimicrobial resistance through more efficient and data-driven therapeutic development strategies.
Hanshal Inagala· Journal of Pharmaceutical Re...· 0 citations
Antimicrobial resistance (AMR) is a critical global health problem that has become increasingly alarming in recent years. The discovery of new antibiotics is one approach for alleviating AMR, however, screening for novel drugs is time consuming and expensive. To accelerate antibiotic discovery, the integration of machine learning algorithms with Quantitative Structure–Activity Relationship (QSAR) calculations could provide a rapid solution. Thus, this study combines a QSAR model and machine learning algorithms to predict antibacterial activities of potential novel drugs based on chemical information. Information on compounds that are reportedly active and inactive against bacteria was downloaded from the PubChem database and manually curated to create positive and negative datasets. The decision tree (DT), support vector machine (SVM), and naïve Bayesian (NB) algorithms were employed to predict the antibacterial activities of chemical compounds from their Simplified Molecular Input Line Entry System (SMILES) information. The models were then evaluated quantitatively and tuned. DT and SVM exhibited comparable predictive performance and outperformed the NB model, achieving accuracy, precision, sensitivity, and AUC-ROC values exceeding 0.90. DT was chosen for further analysis because of its simplicity and effectiveness. This revealed that descriptors relating to the electrotopology and β-lactam structures of compounds were the top contributors to model predictability. The model was then further tested against different classes of antibiotics and achieved high accuracy in all classes. The model is freely available as a web application at: https://antibacterial-predictor-model-ocogzqyibervrqb7trvfev.streamlit.app/.
Jiratchaya Nakbang, Chonthicha Arbsuwan, S. Prom-on et al.· PLOS Digital Health· 0 citations
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