AI Model AVP-Pro Speeds Discovery of Antiviral Peptides

Antiviral peptides have long been viewed as one of the more promising corners of the antiviral toolbox: short chains of amino acids that can interfere with viruses before they gain a foothold in a host cell. Yet identifying which peptides actually possess antiviral activity, and what kind of viral targets they prefer, has remained a slow and expensive experimental problem. A team of researchers in China now reports a computational framework, called AVP-Pro, that aims to compress that search, combining modern protein language models with a two-stage deep learning pipeline that not only flags candidate antiviral peptides but also predicts which virus families and specific viruses they are likely to act against. The work, published in BMC Genomics, was led by Xinru Wen, Weizhong Lin, Zi Liu and Xuan Xiao of the School of Information Engineering at Jingdezhen Ceramic University in Jiangxi Province.

The biological rationale behind the study rests on well-established sequence chemistry. Antiviral peptides tend to share recognizable characteristics: particular amino acid compositions, a net positive charge that helps them interact with negatively charged viral membranes or envelopes, hydrophobicity patterns that govern how they insert into lipid bilayers, and conserved sequence motifs that recur across peptides with similar mechanisms of action. But these features are distributed unevenly and interact in nonlinear ways, which is precisely why simple rule-based screens have struggled. Two peptides can look superficially similar yet differ sharply in antiviral potency, and a peptide that works against one virus family may be inert against another.

Existing computational classifiers, the authors note, have generally treated the problem as a binary one: is a given sequence an antiviral peptide or not? That framing discards valuable information. Because different AVPs exhibit distinct virus-targeting specificities, a binary answer tells a laboratory only half of what it needs to know before committing to synthesis and testing. The team also identified three persistent technical weaknesses in prior methods: difficulty modeling complex, long-range dependencies within peptide sequences; difficulty integrating heterogeneous feature sources, such as learned embeddings and hand-crafted physicochemical descriptors; and difficulty separating highly similar positive and negative samples that crowd the decision boundary between classes.

AVP-Pro addresses the feature-integration problem through what the authors call adaptive multi-representation fusion. The framework draws on two complementary sources of information. The first is a deep sequence representation derived from ESM-2, a large-scale protein language model trained on hundreds of millions of natural protein sequences. Such models learn to encode structural and functional context into dense numerical vectors, capturing subtle patterns that would be difficult to hand-engineer. The second source is a set of ten conventional physicochemical descriptors, the classical encodings of peptide science, covering properties such as charge, hydrophobicity and residue composition. By fusing these representations adaptively, rather than simply concatenating them, the model can weight each information source according to its usefulness for a given input.

Architecture plays a central role in how the framework reads a sequence. Convolutional neural network layers are used to capture local fragment-level features, short contiguous stretches of residues that often carry functional significance. Bidirectional long short-term memory units, or BiLSTM, then sweep across the sequence in both directions, capturing global contextual dependencies that span the full length of the peptide. Self-attention modules sit on top of this, allowing the model to focus dynamically on the positions most informative for the classification decision. An adaptive gating mechanism finally arbitrates among these parallel streams, deciding how much each representation should contribute to the fused output for any particular peptide rather than imposing a fixed weighting scheme across the entire dataset.

Perhaps the most distinctive element of the study is its handling of ambiguous samples. In AVP datasets, positive and negative sequences are often extremely similar, and this similarity produces fuzzy decision boundaries that degrade classifier performance, especially for the borderline cases that matter most in real screening pipelines. The team attacked this from two directions. First, they introduced data augmentation guided by BLOSUM62, the standard amino acid substitution matrix long used in sequence alignment. BLOSUM62 scores encode which residue substitutions are biologically tolerable, so augmenting training data with substitution-informed variants exposes the model to realistic sequence variation without straying into biologically implausible territory. Second, they employed online hard example mining, or OHEM, within a contrastive learning objective. Contrastive learning trains the model to pull similar examples together and push dissimilar ones apart in its internal embedding space; by focusing the loss on the hardest, most easily confused samples, the framework sharpens the boundary where it is thinnest.

The resulting system operates in two stages. In the first stage, AVP-Pro performs general antiviral peptide identification, deciding whether an input sequence is an AVP at all. On the independent test set, the authors report that the model achieved competitive predictive performance, holding its own against existing approaches while offering the richer internal machinery described above. The second stage is where the framework departs more sharply from earlier work. Building on the first-stage model through transfer learning, AVP-Pro predicts functional subtypes: which virus families and which specific viruses a candidate peptide is likely to target. In this stage the framework covered six virus families and eight specific viruses, a level of functional granularity that binary classifiers simply cannot provide.

Across multiple evaluation metrics, the authors report that AVP-Pro showed stable performance in both the general identification task and the functional subtype prediction task on the benchmark datasets they evaluated. Stability across metrics is an important qualifier in this field, since models optimized for a single figure of merit can exhibit lopsided behavior, excelling on accuracy while failing on measures that penalize false negatives or class imbalance. The consistency reported here suggests that the fusion and contrastive components are doing genuine work rather than merely inflating one headline number. The authors position the framework as a tool for sequence-level functional annotation and for prioritizing candidate antiviral peptides before laboratory characterization, a use case in which even modest gains in precision translate into meaningful savings of time and reagent cost.

The broader context makes work of this kind increasingly timely. Peptide-based antivirals occupy an attractive middle ground between small molecules and full-length protein therapeutics: they are typically less immunogenic than antibodies, more specific than broad-spectrum antiviral compounds, and synthetically accessible. But the design space of possible peptide sequences is astronomically large, and experimental screens cover only a vanishing fraction of it. Machine learning filters of the kind embodied in AVP-Pro act as a funnel, narrowing candidate lists so that wet-lab resources are spent on sequences with a computationally justified prior of activity. The addition of subtype prediction pushes the funnel further, allowing researchers to search not just for antiviral activity in general but for activity against a virus family of particular interest.

The study also illustrates a wider trend in computational biology: the convergence of large pretrained protein language models with task-specific deep learning heads and classical domain knowledge. ESM-2 embeddings supply the learned, context-rich representation; BLOSUM62 and physicochemical descriptors supply decades of accumulated biochemical insight; and the attention, recurrent and gating modules supply the flexibility to combine them on a per-instance basis. None of these ingredients is new on its own, but their integration, together with hard-example-focused contrastive training, reflects a maturing methodology for peptide function prediction. The work was supported by grants from the National Natural Science Foundation of China, and the authors declare no competing interests. As with any computational predictor, the model’s judgments will ultimately need experimental validation, but as a screening instrument it offers researchers a substantially finer-grained map of the antiviral peptide landscape than the binary tools that preceded it.

Subject of Research: Machine learning-based identification and functional subtype prediction of antiviral peptides.

Article Title: AVP-Pro: adaptive multi-representation fusion and contrastive learning for antiviral peptide identification and functional subtype prediction

Article References: Wen, X., Lin, W., Liu, Z., & Xiao, X. (2026). AVP-Pro: adaptive multi-representation fusion and contrastive learning for antiviral peptide identification and functional subtype prediction. BMC Genomics. https://doi.org/10.1186/s12864-026-13352-z

Image Credits: AI Generated

DOI: 10.1186/s12864-026-13352-z

Keywords: antiviral peptides, machine learning, ESM-2, contrastive learning, transfer learning, BLOSUM62, BiLSTM, self-attention, OHEM strategy, functional subtype prediction, peptide function prediction, deep learning