Evidence map›Paper›PMID 42770799›Full record

ReviewFEMS microbiology reviews2026

Computational methods in physical virology: a critical perspective across lengths and timescales.

Adolfo B Poma, Luis F Cofas-Vargas, Fernando L Barroso da Silva, Sergio Pantano, Marta Bally

Abstract readReview
In one paragraph

Review in FEMS microbiology reviews, 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

5 authors.

Adolfo B PomaDepartment of Biosystems and Soft Matter, Institute of Fundamental Technological Research, Polish Academy of Sciences, ul. Pawińskiego 5B, 02-106 Warsaw, Poland.
Luis F Cofas-VargasDepartamento de Química, Universidad Autónoma Metropolitana-Iztapalapa, 09310 Mexico City C.P., Mexico.
Fernando L Barroso da SilvaDepartment of Biomolecular Sciences, School of Pharmaceutical Sciences at Ribeirão Preto, University of São Paulo, 14040-900 Ribeirão Preto, São Paulo, Brazil.
Sergio PantanoInstitut Pasteur de Montevideo, Mataojo 2020, 11400 Montevideo, Uruguay.
Marta BallyDepartment of Clinical Microbiology, Wallenberg Centre for Molecular Medicine, Umeå Centre for Microbial Research & and Science for Life Laboratory, Umeå University, 901 87 Umeå, Sweden.

Funding

National Council for Scientific and Technological Development 307461/2025-4National Science Center, Poland 2022/45/B/NZ1/02519São Paulo Research Foundation 2020/07158-2SNI
6 · The paper itself

Abstract

Physical virology investigates viral particles by focusing on their assembly, stability, mechanics, and interactions with host cells, neutralizing antibodies, and surfaces. Within this field, computational virology is becoming an indispensable pillar, serving as a "computational microscope" that bridges the spatio-temporal scales of viral processes, from individual protein dynamics to capsid assembly and cellular entry. This perspective article offers a critical overview of the current state, challenges, and future directions of computational approaches in physical virology. Our vision is anchored in the research presented at the 2025 EMBO/FEBS Lecture Course on Physical Virology held in Sant Feliu de Guixols, Spain, and complemented by a targeted survey among attendees. We survey the principal methodological frameworks in use, from all-atom to multiscale molecular simulations, mesoscale simulations, and growing integration of artificial intelligence (AI) tools. We also critically examine the central obstacles impeding the field's progress, including the computational-experimental gap, limited accessibility to simulation data, reproducibility concerns, and systemic gender and geographic inequities. Finally, we outline future perspectives, proposing that integrating physics-aware AI with multiscale simulation frameworks, combined with community-driven data-sharing initiatives, will transform the computational microscope from a descriptive tool into a predictive engine for antiviral therapies, rational vaccine design, and biotechnological innovation.

Indexed as

Computational BiologyVirologyArtificial Intelligence

Identifiers

PMID42770799
PMCPMC13637490

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.