ReviewFrontiers in bioinformatics2026
Repurposing AI for protein interactions and dynamics: opportunities, limitations, and lessons.
Review in Frontiers in bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- Artificial Intelligence and Protein Design: A retrospective study on 20-year emerging trends and core research areas from bibliometric perspectives.Probiotics and antimicrobial proteins · 2026Article
- Artificial Intelligence and Protein Design: A retrospective study on 20-year emerging trends and core research areas from bibliometric perspectives.Probiotics and antimicrobial proteins · 2026Article
- Disrupting the Undruggable: Emerging Modalities for Targeting Protein-Protein Interactions in Oncology.Biology · 2026Review
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
No grant is acknowledged in the PubMed record.
Abstract
Understanding protein interactions and dynamics of biological systems is central in drug discovery. Advances in artificial intelligence (AI) have expanded the scope of predictive learning for complex biological systems. Repurposing current gold-standard AI algorithms for structural and biological applications illustrates how flexible and powerful these approaches can be. In this mini-review, we examine how AI models are repurposed across domains and analyze how inductive biases, learning objectives, and representation choices inherited from their original applications shape performance in protein interaction and dynamics tasks. We discuss where AI approaches succeed, where they systematically fail, and how their behavior differs from physics-based modeling. We further highlight unresolved biological challenges, data and benchmarking limitations, and emerging opportunities for hybrid AI-physics workflows that balance efficiency with physical realism. By framing recent developments through a cross-domain adaptation framework, this review aims to provide practical guidance for selecting, evaluating, and integrating AI models in protein interaction and dynamics studies, and to support more reliable and biologically meaningful applications of AI in computational protein science.
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What OpenQuestion holds
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.