{"ID":2878736,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.18446","arxiv_id":"2508.18446","title":"From Prediction to Simulation: AlphaFold 3 as a Differentiable Framework for Structural Biology","abstract":"AlphaFold 3 represents a transformative advancement in computational biology, enhancing protein structure prediction through novel multi-scale transformer architectures, biologically informed cross-attention mechanisms, and geometry-aware optimization strategies. These innovations dramatically improve predictive accuracy and generalization across diverse protein families, surpassing previous methods. Crucially, AlphaFold 3 embodies a paradigm shift toward differentiable simulation, bridging traditional static structural modeling with dynamic molecular simulations. By reframing protein folding predictions as a differentiable process, AlphaFold 3 serves as a foundational framework for integrating deep learning with physics-based molecular","short_abstract":"AlphaFold 3 represents a transformative advancement in computational biology, enhancing protein structure prediction through novel multi-scale transformer architectures, biologically informed cross-attention mechanisms, and geometry-aware optimization strategies. These innovations dramatically improve predictive accura...","url_abs":"https://arxiv.org/abs/2508.18446","url_pdf":"https://arxiv.org/pdf/2508.18446v1","authors":"[\"Alireza Abbaszadeh\",\"Armita Shahlaee\"]","published":"2025-08-25T19:49:28Z","proceeding":"q-bio.BM","tasks":"[\"q-bio.BM\",\"cs.LG\"]","methods":"[\"Transformer\"]","has_code":false}