AlphaFold is a groundbreaking method for predicting the three-dimensional structure of proteins with high accuracy. Proteins are essential to life, and understanding their structure helps in understanding their function. However, determining protein structures experimentally is time-consuming and resource-intensive, limiting the number of known structures. AlphaFold, a neural network-based model, has demonstrated the ability to predict protein structures with atomic accuracy, even when no similar structures are available. This was validated in the 14th Critical Assessment of protein Structure Prediction (CASP14), where AlphaFold outperformed other methods. The model incorporates physical and biological knowledge, using multi-sequence alignments and deep learning to predict structures accurately. AlphaFold's success lies in its ability to handle complex proteins, including those with no structural homologues, and it provides reliable confidence scores for predictions. The model is scalable and can predict structures for very long proteins. AlphaFold's architecture includes a novel neural network called Evoformer, which processes multiple sequence alignments and pairwise features to produce accurate predictions. The model also uses iterative refinement and self-distillation to improve accuracy. AlphaFold has shown high accuracy on recent PDB structures and is capable of predicting structures for a wide range of proteins, including homomers and complex proteins. The model's success represents a significant advancement in structural bioinformatics, enabling large-scale structural studies and applications in biology.AlphaFold is a groundbreaking method for predicting the three-dimensional structure of proteins with high accuracy. Proteins are essential to life, and understanding their structure helps in understanding their function. However, determining protein structures experimentally is time-consuming and resource-intensive, limiting the number of known structures. AlphaFold, a neural network-based model, has demonstrated the ability to predict protein structures with atomic accuracy, even when no similar structures are available. This was validated in the 14th Critical Assessment of protein Structure Prediction (CASP14), where AlphaFold outperformed other methods. The model incorporates physical and biological knowledge, using multi-sequence alignments and deep learning to predict structures accurately. AlphaFold's success lies in its ability to handle complex proteins, including those with no structural homologues, and it provides reliable confidence scores for predictions. The model is scalable and can predict structures for very long proteins. AlphaFold's architecture includes a novel neural network called Evoformer, which processes multiple sequence alignments and pairwise features to produce accurate predictions. The model also uses iterative refinement and self-distillation to improve accuracy. AlphaFold has shown high accuracy on recent PDB structures and is capable of predicting structures for a wide range of proteins, including homomers and complex proteins. The model's success represents a significant advancement in structural bioinformatics, enabling large-scale structural studies and applications in biology.