AlphaFold3 is Google DeepMind's official command-line application for running AlphaFold 3 biomolecular structure prediction inference. The dominant character is a production-grade scientific application, though it is heavily intertwined with research data pipelines: it ingests a JSON job specification describing protein, RNA, DNA, and ligand chains, runs Jackhmmer/Nhmmer searches over large genetic databases, builds multiple sequence alignments, searches structural templates with HMMER, featurizes the input into JAX/Haiku tensors, samples structures with a diffusion model, and writes predicted structures as mmCIF files plus confidence JSON (pLDDT, PAE, pTM, ipTM). The codebase is not a training repository; it provides inference only and requires separately downloaded model parameters under restrictive legal terms. Architecturally, the system is a multi-stage pipeline driven by the `run_alphafold.py` entry point. Core modules include the data pipeline (`src/alphafold3/data/pipeline.py`), MSA/template tools (`src/alphafold3/data/tools/`), a structure representation layer (`src/alphafold3/structure/`), JAX geometric primitives (`src/alphafold3/jax/geometry/`), the neural network model (`src/alphafold3/model/`), and performance-critical C++ pybind11 extensions for mmCIF parsing, MSA feature extraction, and string/array operations. It depends on external binaries (Jackhmmer, Nhmmer, HMMER suite), RDKit for ligand conformer generation, JAX/Haiku for model execution, and ~630 GB of downloadable sequence and structure databases. The repository is actively maintained, with recent commits and CI running CPU-only data tests. Technical drift is low for a research release: dependencies are pinned via `pyproject.toml` and `uv.lock`, and active maintenance keeps it aligned with its ecosystem, though the C++ build, GPU-specific requirements, and external database URLs remain fragility points.
Protenix is a production-oriented PyTorch implementation of AlphaFold 3 for biomolecular structure prediction, maintained by ByteDance. It is best classified as an application rather than a pure research codebase: it ships a `protenix` CLI with commands for prediction, input conversion, MSA/template/RNA-MSA search, and data preparation, plus training/fine-tuning entry points, batch inference runners, Docker deployment, web-service helpers, and PyPI packaging. The code ingests PDB/CIF, FASTA, SMILES/SDF, and JSON descriptors covering proteins, DNA, RNA, ligands, ions, modified residues, covalent bonds, and contact/pocket constraints, then outputs predicted 3D CIF structures with confidence metrics such as pLDDT, PAE, pTM, and ipTM.
Chai-1 is a molecular structure prediction tool that takes FASTA sequences (proteins, DNA, RNA, ligands, glycans) and predicts 3D structures using a diffusion model. It is packaged as a Python library with a CLI (`chai-lab fold`) and a Python API (`chai_lab.chai1.run_inference`). The pipeline ingests sequences, optionally generates MSAs via ColabFold server, loads templates from RCSB, computes ESM embeddings, applies user-defined restraints (contacts, pockets, covalent bonds), tokenizes all atoms, generates a rich set of features, collates them into batches, and runs a TorchScript diffusion model to produce multiple structure samples. Outputs are ranked by confidence metrics (pTM, ipTM, pLDDT, clash score) and saved as CIF files. The codebase is well-structured with clean abstractions (feature generators, data contexts, collation), strong typing (jaxtyping, beartype), and CI quality gates. It is a hybrid of a production-grade inference tool and research code, with some copied components (rigid transformations from AlphaFold, ColabFold MSA server) and a monolithic inference entry point. The repository is actively maintained with recent commits, PyPI releases, and comprehensive examples.