ArticleDigital discovery2025
af2rave: protein ensemble generation with physics-based sampling.
Article in Digital discovery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- The Advantages of AI for Computational Protein Studies and Looking Ahead at the Next Challenges: Single Structures Are Not Enough.Journal of molecular biology · 2026Review
- Proton hopping and secondary gating reveal a universal transport mechanism in a minimal bacterial transporter EmrE.Biophysical journal · 2026Article
- AI-Physics-Experiment Trinity for Integrated Protein Dynamics Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- MAHLER: Integrating Metadynamics and Inverse Folding to Predict Antibody-Antigen Kinetics.bioRxiv : the preprint server for biology · 2026Article
- Free Energy Calculation Method Based on Enhanced Sampling of Diverse Protein Conformations Predicted by Artificial Intelligence.The journal of physical chemistry letters · 2026Article
- Latent thermodynamic flows: unified representation learning and generative modeling of temperature-dependent behaviors from limited data.Chemical science · 2026Article
- Article
- DyVarMap: Integrating Conformational Dynamics and Interpretable Machine Learning for Cancer-Associated Missense Variant Classification in FGFR2.Bioengineering (Basel, Switzerland) · 2026Article
- Reinforced molecular dynamics: Physics-infused generative machine learning model simulates protein motion.PNAS nexus · 2026Article
- Approaches for regulating enzyme activities: Recent advances in experiment and computation.Current opinion in structural biology · 2025Review
- Hierarchical AF2RAVE for Multiconformation Virtual Screening Targeting S100 CaJournal of chemical theory and computation · 2025Article
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
We introduce , an open-source Python package that implements an improved and automated version of our previous AlphaFold2-RAVE protocol. AlphaFold2-RAVE integrates machine learning-based structure prediction with physics-driven sampling to generate alternative protein conformations efficiently. It has been well established that protein structures are not static but exist as ensembles of conformations, many of which are functionally relevant yet challenging to resolve experimentally. While deep learning models like AlphaFold2 can predict structural ensembles, they lack explicit physical validation. The Alphafold2-RAVE family of methods addresses this limitation by combining reduced multiple sequence alignment (MSA) AlphaFold2 predictions with biased or unbiased molecular dynamics (MD) simulations to efficiently explore local conformational space. Compared to our previous work, the current workflow significantly reduced the required amount of
Identifiers
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.