AlphaFold 3: Architecture and Applications in Protein–Ligand Complex Prediction

Abstract

The interaction between proteins and ligands is the core mechanism of biological activities and drug development. In traditional experimental methods, there are a series of problems such as long research cycles, high costs, and limited precision. In the rapid way of artificial intelligence (AI) era, AI tools have driven the progress of biological science. Therefore, this article focuses on elaborating on the new AI tool Alphafold to analyze the three-dimensional spatial structure of proteins. Especially Alphafold3(AF3), which adds diffusion algorithms, confidence assessment units, and the Pairformer module to the original AI model and simplifies the calculation steps, has achieved a revolutionary leap from single atom or protein homologous sequence pairing output to position pairing output between nucleic acids - proteins and peptide residues. It has realized the all-round design of protein-ligand complexes throughout the process. At the same time, this article systematically reviews the technical architecture and core algorithm modules of AlphaFold3, and looks forward to the application progress in the construction of protein-ligand complex structures, including the improvement in the confidence of pocket-type ligands paired with G protein-coupled receptors compared to AF2. The conclusion indicates that AF3 can be widely applied in biological fields such as life health and medical technology.