NeXtMD: a new generation of machine learning and deep learning stacked hybrid framework for accurate identification of anti-inflammatory peptides

Abstract

Background: Accurate identification of anti-inflammatory peptides (AIPs) is crucial for drug development and inflammatory disease treatment. However, the short length and limited informational content of peptide sequences make precise computational recognition particularly challenging. While various machine learning and deep learning approaches have been explored, their limitations in feature representation and model integration hinder the effective discovery of novel AIPs.

Results: In this study, we present NeXtMD, a novel dual-module stacked framework that integrates both machine learning (ML) and deep learning (DL) components for accurate AIP identification. NeXtMD systematically extracts four functionally relevant sequence-derived descriptors-residue composition, inter-residue correlation, physicochemical properties, and sequence patterns-and utilizes a two-stage prediction strategy. The first stage generates preliminary predictions using four distinct encoding strategies and ML classifiers, while the second stage employs a multi-branch residual network (ResNeXt) to refine prediction outputs. Benchmark evaluations demonstrate that NeXtMD outperforms current state-of-the-art methods on multiple performance metrics. Moreover, NeXtMD maintains strong generalization capabilities when applied to unseen peptide sequences, showing its robustness and scalability.

Conclusions: NeXtMD offers a high-performance and interpretable computational framework for AIP identification, with significant potential to facilitate the discovery and design of peptide-based anti-inflammatory therapeutics. The architecture and methodological innovations of NeXtMD also provide a generalizable strategy that can be adapted to other bioactive peptide prediction tasks, supporting broader applications in therapeutic peptide development.

Keywords: Anti-inflammatory peptide; Bioinformatics; Deep learning; Machine learning.

Fig. 1

Schematic illustration of the NeXtMD framework. A Overview of the NeXtMD architecture, which comprises four main components: feature extraction, initial prediction using machine learning classifiers, feature refinement via a multi-layer grouped residual network, and final classification. B Representation of the first module, including feature fusion and predictions generated by four distinct machine learning classifiers (RF, XGBoost, LightGBM, and GBDT). C Structure of Residual Networks neXt (ResNeXt) module, consisting of five sequential layers with multiple residual blocks, followed by the terminal classification layer

Fig. 2

Analysis of the AIPs dataset. A Sequence length distribution comparison between positive (AIPs) and negative (non-AIPs) samples within the dataset. B Average amino acid composition profiles of 20 canonical amino acids in AIPs and non-AIPs. C Heatmap visualization depicting the distribution of amino acid attribution scores derived from randomly selected sequences in the AIP dataset. D Heatmap visualization depicting the distribution of amino acid attribution scores derived from randomly selected sequences in the non-AIP dataset. E Positional preference of conserved residues identified within AIP sequences. F Positional preference of conserved residues identified within non-AIP sequences

Fig. 3

Results of the NeXtMD model section with integrated comparison and fivefold cross-validation. A ROC curves obtained by a single ML algorithm. B ROC curves obtained from meta-feature inputs of ResNeXt using a single ML algorithm. C ROC curve of NeXtMD model under fivefold cross-validation. D PRC curve of NeXtMD model under fivefold cross-validation

Fig. 4

Comparison of NeXtMD with other methods on AIP test set

Fig. 5

Visualization of the test set's original features and the learned representations obtained by NeXtMD. Red points represent positive (AIP) samples and blue points represent negative (non-AIP) samples. A shows the t-SNE visualization of the original features obtained from the test set. B shows the t-SNE visualization of the learned features after NeXtMD was trained on the AIP samples. C shows the UMAP visualization of the original features obtained from the test set. D shows the UMAP visualization of the learned features after NeXtMD was learning the AIP database samples

Fig. 6

Comparison ablation test results to validate the contribution of ML algorithms and feature selection in NeXtMD models. A Histogram plots showing the performance of NeXtMD when each of the four ML models is ablated one at a time (retaining the other three). B Histogram plots showing performance when each of the four feature descriptors is ablated one at a time. C ROC curves of NeXtMD with one ML models removed at a time. D ROC curves of NeXtMD with one feature descriptor removed at a time

Fig. 7

Performance analysis of NeXtMD on external test sets. A ROC curves of NeXtMD evaluated on the DeepAIP-derived external test set containing unfamiliar AIPs. B ROC curves of NeXtMD evaluated on the BertAIP-derived external test set containing unfamiliar AIPs. C ROC curves of NeXtMD evaluated on the AIPs-DeepEnC-derived external test set containing unfamiliar AIPs. D Radar plot omparing performance on the internal test set versus external test sets across evaluation metrics

Fig. 8

Effectiveness of AIP dataset enhancement based on DeepAIP exogenous AIPs and comparison with initial AIP dataset. A ROC curves of NeXtMD trained and evaluated on the enhanced AIP dataset. B Radar plot comparing the initial AIP dataset and the enhanced dataset across evaluation metrics. C Histogram comparing the distribution of evaluation metrics between the initial and enhanced datasets

Fig. 9

Validation of NeXtMD’s model transferability to AMP prediction. A ROC curves of NeXtMD evaluated on the AMP dataset. B Radar chart comparing NeXtMD, TriStack, and TriNet on the AMP dataset. C Histogram comparing the performance of NeXtMD, TriStack, and TriNet on the AMP dataset