AlphaFold 3 is a deep learning model that significantly improves the accuracy of predicting the structures of biomolecular complexes, including proteins, nucleic acids, small molecules, ions, and modified residues. The model uses an updated diffusion-based architecture, which allows for more accurate predictions across a wide range of biomolecular interactions. Compared to previous specialized tools, AlphaFold 3 demonstrates higher accuracy in predicting protein-ligand interactions, protein-nucleic acid interactions, and antibody-antigen interactions. The model's architecture includes a Pairformer Module that replaces the Evoformer from AlphaFold 2, and a Diffusion Module that directly predicts raw atom coordinates. This approach allows the model to handle a wide variety of chemical structures without excessive special casing. The model also includes confidence measures that predict atom-level and pairwise errors in the final structures. These measures are based on the predicted structures and help in assessing the reliability of the predictions. AlphaFold 3 was evaluated on various benchmarks, including the PoseBusters benchmark set for protein-ligand interfaces, and it outperformed classical docking tools like Vina and RoseTTAFold All-Atom. The model also showed improved accuracy in predicting protein-nucleic acid complexes and RNA structures compared to other methods. Despite its improvements, AlphaFold 3 has some limitations, including issues with stereochemistry, hallucinations in disordered regions, and the inability to predict dynamic behavior of biomolecular systems. The model's performance is also affected by the number of training samples and the complexity of the interactions being predicted. However, the model's ability to handle a wide range of biomolecular interactions within a unified framework demonstrates the potential of deep learning in accurately predicting biomolecular structures. The development of AlphaFold 3 highlights the importance of deep learning in advancing the field of structural biology and has the potential to significantly reduce the amount of data required for accurate predictions.AlphaFold 3 is a deep learning model that significantly improves the accuracy of predicting the structures of biomolecular complexes, including proteins, nucleic acids, small molecules, ions, and modified residues. The model uses an updated diffusion-based architecture, which allows for more accurate predictions across a wide range of biomolecular interactions. Compared to previous specialized tools, AlphaFold 3 demonstrates higher accuracy in predicting protein-ligand interactions, protein-nucleic acid interactions, and antibody-antigen interactions. The model's architecture includes a Pairformer Module that replaces the Evoformer from AlphaFold 2, and a Diffusion Module that directly predicts raw atom coordinates. This approach allows the model to handle a wide variety of chemical structures without excessive special casing. The model also includes confidence measures that predict atom-level and pairwise errors in the final structures. These measures are based on the predicted structures and help in assessing the reliability of the predictions. AlphaFold 3 was evaluated on various benchmarks, including the PoseBusters benchmark set for protein-ligand interfaces, and it outperformed classical docking tools like Vina and RoseTTAFold All-Atom. The model also showed improved accuracy in predicting protein-nucleic acid complexes and RNA structures compared to other methods. Despite its improvements, AlphaFold 3 has some limitations, including issues with stereochemistry, hallucinations in disordered regions, and the inability to predict dynamic behavior of biomolecular systems. The model's performance is also affected by the number of training samples and the complexity of the interactions being predicted. However, the model's ability to handle a wide range of biomolecular interactions within a unified framework demonstrates the potential of deep learning in accurately predicting biomolecular structures. The development of AlphaFold 3 highlights the importance of deep learning in advancing the field of structural biology and has the potential to significantly reduce the amount of data required for accurate predictions.