Evidence map›Paper›PMID 42108305›Full record

ReviewMethods in molecular biology (Clifton, N.J.)2026

Protein Language Models in Virology: A Review of Advances and Applications.

Lingxin Luo, Yixue Li, Tao Huang

Abstract readReview
PubMed Publisher
In one paragraph

Review in Methods in molecular biology (Clifton, N.J.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Lingxin LuoDepartment of Artificial Intelligence and Digital Health, CAS Engineering Laboratory for Nutrition, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
Yixue LiDepartment of Artificial Intelligence and Digital Health, CAS Engineering Laboratory for Nutrition, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China. li_yixue@gzlab.ac.cn.
Tao HuangDepartment of Artificial Intelligence and Digital Health, CAS Engineering Laboratory for Nutrition, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China. huangtao@sinh.ac.cn.ORCID https://orcid.org/0000-0003-1975-9693

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Protein language models (PLMs) enable functional analysis of divergent viral sequences without homology alignment. This review covers PLM architectures from sequence encoders through structure-aware architectures to generative models and assesses their application to orphan protein structure resolution, virosphere-wide functional classification, host factor identification, and therapeutic antibody optimization. Finally, the limitations of the current model in terms of interpretability and insufficient data representation are discussed while exploring future trends toward multimodal integration and the "dry-wet" experimental loop to accelerate the adoption of artificial intelligence in precision virology.

Indexed as

Viral ProteinsVirologyVirusesGenerative Artificial IntelligenceHumansLarge Language ModelsModels, MolecularViral ProteinsAntiviral strategyDeep learningProtein language modelsViral dark matterVirologyVirus-host interactions

Identifiers

PMID42108305

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.