Score breakdown
Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.
Why it matters
Useful for drug-discovery teams, structural-bioinformatics teams, AI/ML teams working on protein design, biotech founders, AI-curious readers tracking open biomolecular-AI, and any developer wiring an AI coding agent to AlphaFold3-class co-folding workflows -- and who can pair aurekaresearch/OpenDDE with Python 3.11+ + uv for the runtime surface, PyTorch (CUDA 12.6 / CPU) for the inference surface
Who should use it
Who should skip it
Skip aurekaresearch/OpenDDE if the source link, documentation, or setup requirements do not align with your current workflow or stack.
About this signal
aurekaresearch/OpenDDE is tracked by RepoRadar as a apache-2.0 open-source all-atom in the Open-Source Biomolecular Foundation Model for Dr section. It was first seen on 2026-07-07 and last updated on 2026-07-07. The current verdict is 'try now' with a Gold tier and advanced setup difficulty. aurekaresearch/OpenDDE leads on workflow potential (9.3) and novelty (9.0); its lowest signal is setup ease (4.2), so factor that in before investing setup time. This page summarizes the evidence RepoRadar has captured from captured source metadata. The score, tier, risk label, and verdict on this page are never influenced by sponsorship, ads, or tips — they reflect only the usefulness, popularity, novelty, momentum, maturity, and evidence signals described in the RepoRadar methodology.
How this item is evaluated
RepoRadar assigned aurekaresearch/OpenDDE a composite score of 8.2 out of 10, placing it in the Gold tier. This score combines weighted sub-signals: usefulness (35%), novelty (18%), momentum (14%), maturity (10%), open-source/build quality (7%), evidence quality (6%), workflow potential (6%), and setup ease (4%). Popularity is tracked separately at 1.0 and never affects the composite score or tier. The risk label of 'conditional' reflects inherent user-impacting hazards, not generic novelty. Items with no risk flag may still require normal code review before production use.
Putting this into practice? Read How to read AI benchmarks without getting fooled for the checklist behind this score.
Risk explanation
The 245* / 26-fork repo is brand new (days old) and at active maintenance but the status is a preview release -- the README explicitly says 'CLI flags; input/output JSON fields; and released checkpoints may change between versions; and predictions are not guaranteed to be reproducible across releases. It is not yet intended for production pipelines'.