OpenFold

OpenFold is a high-performance, PyTorch-based reproduction of DeepMind’s AlphaFold 2, developed by the AlQuraishi Lab and the OpenFold Consortium. As of 2026, it remains the definitive open-source platform for researchers who require not only inference but the ability to retrain or fine-tune protein folding models on proprietary datasets. Unlike the original AlphaFold 2 release, which was primarily optimized for inference via JAX, OpenFold provides a memory-efficient implementation using Triton kernels and DeepSpeed-inspired optimizations, making it accessible for institutions with standard GPU clusters. The architecture utilizes a highly optimized 'Evoformer' stack and a structure module that predicts 3D coordinates of all atoms in a protein. Its market position in 2026 is critical for 'private-cloud' drug discovery, where pharmaceutical entities use OpenFold to maintain data sovereignty while performing massive-scale virtual screening. It supports both monomer and multimer configurations, providing GDT_TS scores effectively identical to AlphaFold 2 while offering superior flexibility for experimental research in protein-protein interactions and enzyme design.