Accurate structure prediction of biomolecular interactions with AlphaFold 3

AlphaFold 3 (Nature, May 2024) expands the original AlphaFold2 to predict structures not just of proteins but of the full range of biomolecular interactions — proteins with DNA, RNA, small molecules (ligands), and modified amino acids. The architecture shifts from the Evoformer to a diffusion-based model, generating structural hypotheses by iteratively denoising from Gaussian noise rather than predicting coordinates directly. This enables AlphaFold 3 to handle the diversity of molecular types that couldn't be represented in the original residue-pair framework. On PoseBusters benchmarks (drug-like molecule docking), it outperforms specialized docking tools by a wide margin. The practical implication: AlphaFold 3 can model the molecular basis of drug-protein binding, protein-DNA interactions in gene regulation, and enzyme catalysis mechanisms — dramatically widening the scope of AI-assisted drug discovery and molecular biology. The paper also announces the AlphaFold Server, making predictions available freely for academic non-commercial use.