Unified API for protein language models (ESM3) and structure prediction (AlphaFold2). Run locally on your hardware or scale to cloud GPUs — with built-in LLM integration and interactive 3D visualization.
A single gateway routes requests to the right model backend. LLMs connect through an MCP server — a standard protocol that lets AI assistants call your tools directly — over a private VPN. GPU resources auto-stop when idle.
A single API routes to ESM3, AlphaFold2, and ESMFold with automatic health checks and failover. Submit long-running predictions to an async job queue with progress tracking, or run fast queries synchronously.
All models are exposed as conversational tools that any AI assistant can call directly. Predict structures, compare variants, and generate interactive 3D views, per-residue confidence plots, and sequence alignments — all through natural language.
Run models on your own hardware (CPU or GPU) with no cloud dependency, or scale to cloud endpoints that auto-stop when idle.
All model services support NVIDIA (CUDA) and AMD (ROCm) GPUs, with automatic CPU fallback. One config switch.
All infrastructure runs on private networks behind a zero-trust VPN. No public endpoints. Your sequences stay on your machines.
Automated installers for Windows and Linux. One-command setup handles dependencies, GPU drivers, and deployment.
Each model serves a different stage of the protein design workflow, from rapid screening to high-accuracy structure prediction. All run locally or in the cloud.
Connect any AI assistant and work with protein models through natural language. Submit prediction jobs, track progress, generate 3D structure views, and compare variants — all conversationally.
Works with Claude, GPT-4, and any LLM that supports the Model Context Protocol (MCP) — the open standard for giving AI assistants access to external tools.
> Predict the structure of MKFLILLFNILCLFPVLAADNH and show me the result
submitting prediction job...
Job af-22a7b3 submitted
Status: running (12/22 residues processed)
job complete — retrieving results...
Avg. confidence (pLDDT): 87.4 — high
22 residues, 2546.1 Da
generating 3D visualization...
Interactive viewer ready (confidence coloring)
Confidence plot saved
Protein AI Platform is developed and maintained by Eugenio La Cava. The codebase is tested with 200+ automated tests spanning unit, integration, chaos, and property-based testing. The platform is designed for production use in academic and commercial protein research workflows.
Academic and non-profit teams get full access at no cost. Commercial licensing is available for companies building on the platform.
Universities, non-profits, personal learning, open-source
Pharma, biotech R&D, SaaS deployment