ReviewFEMS microbiology reviews2026
Computational methods in physical virology: a critical perspective across lengths and timescales.
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.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
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.
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Registered trials
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.