Hi everyone,

Over the past few months, I’ve been building VORA-X—an open-source, local-first biomolecular AI platform designed to predict 3D protein structures entirely on local infrastructure without relying on paid external cloud APIs.

Why I Built This

While cloud structure prediction platforms are powerful, early-stage drug discovery and biotech research often require complete data privacy for proprietary targets, zero recurring per-prediction API costs, and full local control over the underlying neural architecture.

Key Architectural Components:

• Sequence Representation: Uses local Meta ESM-2 (fair-esm) transformer embeddings extracted directly from raw FASTA inputs.
• 3D Structural Engine: Score-based SE(3) diffusion backbone modeling rigid-body spatial transformations (SO(3) unit quaternions + 3D coordinate translations).
• Local Operations: Built entirely on PyTorch tensor math and open model weights (incorporating Chai-1 parity principles).
• Validation & Metrics: Evaluated using Kabsch alignment for RMSD structural comparison and internal pLDDT residue confidence scoring.
• Stack: Python, PyTorch, SciPy, NumPy, with a FastAPI backend and Streamlit / Web UI.