Protenix: Advancing Structure Prediction Through a Comprehensive AlphaFold3 Reproduction @ByteDanceOSS 

- Protenix, an open-source model by ByteDance, achieves SOTA in predicting various biomolecular structures, outperforming AF3, AF2.3, and RF2NA in multiple benchmarks.

- The team refines AF3's methodology, corrects errors, and shares reproduction insights, also improving the confidence head architecture for better prediction accuracy.

- Protenix enhances accessibility with open-sourced model weights, inference and trainable code, plus a comprehensive dataset release for easy research integration.

- Performance-wise, Protenix excels in protein-ligand cofolding, protein interfaces, and nucleic acid target predictions, showing high accuracy even without similar training structures.

- Case studies highlight Protenix's potential in biological and pharmacological research, though limitations suggest the need for more OOD testing for generalizability.

- Future plans include continuous model improvement, adding features like prior-based predictions, and potentially releasing an evaluation platform. 

💻Code:📜Paper:#Protenix #AlphaFold3 #StructurePrediction #OpenSource #AI4Science