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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2508.18446 |
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| _version_ | 1866918130540871680 |
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| author | Abbaszadeh, Alireza Shahlaee, Armita |
| author_facet | Abbaszadeh, Alireza Shahlaee, Armita |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_18446 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Prediction to Simulation: AlphaFold 3 as a Differentiable Framework for Structural Biology Abbaszadeh, Alireza Shahlaee, Armita Biomolecules Machine Learning 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 |
| title | From Prediction to Simulation: AlphaFold 3 as a Differentiable Framework for Structural Biology |
| topic | Biomolecules Machine Learning |
| url | https://arxiv.org/abs/2508.18446 |