The paper introduces AlphaFold 3 (AF3), an advanced model for predicting the structures of biomolecular complexes, including proteins, nucleic acids, small molecules, ions, and modified residues. AF3 is an evolution of the AlphaFold 2 architecture, designed to improve accuracy and generalization across a wide range of biomolecular interactions. The new model demonstrates significantly improved accuracy over previous tools, particularly in protein-ligand interactions, protein-nucleic acid interactions, and antibody-antigen predictions. The architecture includes a Pairformer Module for processing pair representations and a Diffusion Module for predicting raw atom coordinates, which enhances data efficiency and reduces the need for multiple sequence alignment (MSA) processing. The training process involves a generative diffusion approach, which helps avoid issues like hallucination in disordered regions. Confidence measures are also developed to assess the quality of predictions. Despite these advancements, the model still faces limitations, such as stereochemical violations and challenges in predicting conformational states for certain targets. Overall, AF3 represents a significant step forward in the accurate prediction of biomolecular structures and interactions.The paper introduces AlphaFold 3 (AF3), an advanced model for predicting the structures of biomolecular complexes, including proteins, nucleic acids, small molecules, ions, and modified residues. AF3 is an evolution of the AlphaFold 2 architecture, designed to improve accuracy and generalization across a wide range of biomolecular interactions. The new model demonstrates significantly improved accuracy over previous tools, particularly in protein-ligand interactions, protein-nucleic acid interactions, and antibody-antigen predictions. The architecture includes a Pairformer Module for processing pair representations and a Diffusion Module for predicting raw atom coordinates, which enhances data efficiency and reduces the need for multiple sequence alignment (MSA) processing. The training process involves a generative diffusion approach, which helps avoid issues like hallucination in disordered regions. Confidence measures are also developed to assess the quality of predictions. Despite these advancements, the model still faces limitations, such as stereochemical violations and challenges in predicting conformational states for certain targets. Overall, AF3 represents a significant step forward in the accurate prediction of biomolecular structures and interactions.