Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
September 10, 2025Eurasian Journal of Science Engineering and Technology

Learning molecular machines by machine learning

View Full Paper
Ask AI
Bookmark
Share

Authors

RÇRumeysa Hilal ÇelikHİHacı Aslan Onur İşcilEBEcem Bulut

Discussion

Loading...

Member takes

Overview

Review highlights ML techniques, particularly AlphaFold, for improving protein structure understanding, suggesting significant implications for structural biology.

Key Points

  • Machine learning shows potential to enhance protein structure prediction, bridging the sequence-structure gap.
  • AlphaFold has achieved unprecedented levels of prediction accuracy, impacting drug discovery and protein interactions.
  • ML-based approaches, including AlphaFold, RoseTTAFold, and ESMFold, advance our understanding of protein function.
  • Despite limitations in modeling certain proteins, ongoing improvements in ML algorithms promise better accuracy in structural biology.

Cite This Study

Çelik et al. (2025) studied this question.

synapsesocial.com/papers/68c23e02b210217d64790e93https://doi.org/10.55696/ejset.1620495
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1The Role of Artificial Intelligence in Protein Structural and Functional Prediction: Current Status and Future Prospective2025
  2. 2AI and Machine Learning in Biology: From Genes to Proteins2025
  3. 3Generative AI techniques for conformational diversity and evolutionary adaptation of proteins2025 · 6 citations
  4. 4Of Revolutions and Roadblocks: The Emerging Role of Machine Learning in Biocatalysis2025
  5. 5Advances and challenges in multiscale biomolecular simulations: artificial intelligence‐driven paradigm shift2025