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June 20, 2026BioData MiningOpen Access

Navigating the uncharted: AI-driven advances in protein structure, dynamics, interactions and ligand interactions for understudied families

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Authors

SSShivani SinghRSRuhani SinghSSSunita Sharma

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Overview

Review demonstrates AI's role in predicting protein structures and dynamics, highlighting its significance for neglected families.

Key Points

  • This review examines how AI and machine learning can improve the modeling of underexplored protein families.
  • Reviewed advancements from classical modeling to AI techniques like AlphaFold2 and RoseTTAFold.
  • Highlighted computational strategies for predicting protein-protein and protein-ligand interactions.
  • Presented case studies on G-protein-coupled receptors and intrinsically disordered regions.
  • AI models achieved near-experimental accuracy in predicting protein structures and dynamics.
  • Identified key challenges including the need for experimental validation and ethical AI considerations.
  • Outlined best practices for computational modeling and visualization in structural biology.

Cite This Study

Singh et al. (2026) studied this question.

synapsesocial.com/papers/6a362de1db0793dc1a535dcehttps://doi.org/10.1186/s13040-026-00577-7
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