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Technical Proposal: Closed-Loop ‘AI Scientist’ Orchestration

 

Technical Proposal: Closed-Loop ‘AI Scientist’ Orchestration

Target: TCG Portfolio Startups (Biotech & Generative Biology)

Core Stack: NVIDIA NemoClaw + Microsoft EvoDiff + UniRef90

Objective: Transitioning from "Human-in-the-Loop" to "Autonomous Discovery" (ASI Phase 1).

1. Executive Summary

Currently, the "Design-Build-Test" cycle in protein engineering is bottlenecked by manual data handoffs between computational models (EvoDiff) and laboratory automation (Hamilton/Tecan robots). This proposal details a Unified Orchestration Layer using NVIDIA NemoClaw to serve as the "Cognitive Controller." NemoClaw will autonomously trigger EvoDiff for sequence generation, validate designs via AlphaFold 3 NIMs, and issue execution commands to wet-lab APIs.

2. Architecture Components

A. The Generative Engine (EvoDiff + UniRef90)

  • Role: Sequence "Architect."

  • Action: Utilizes Discrete Diffusion to generate novel amino acid sequences. By training on UniRef90, the model ensures evolutionary plausibility while exploring the "Dark Proteome" (IDRs).

B. The Secure Orchestrator (NVIDIA NemoClaw)

  • Role: The "Brain" and "Security Guard."

  • Action: * Context Management: NemoClaw maintains the "Project Memory," tracking which sequences failed in previous lab runs.

    • Tool-Use: It autonomously calls the Wet-Lab API (via JSON-RPC) to schedule liquid handling.

    • Safety: Ensures generated sequences do not match known biothreat signatures (Screening Protocol).

C. The Validation Gate (NVIDIA NIMs)

  • Role: The "Digital Filter."

  • Action: Before robotic synthesis, NemoClaw routes sequences to an AlphaFold 3 NIM to predict pLDDT scores. Only sequences with high structural confidence proceed to the robot.

3. The "AI Scientist" Workflow (Closed-Loop)

  1. Hypothesis Generation: NemoClaw identifies a target (e.g., a specific viral protease) and prompts EvoDiff to generate 1,000 candidate binders.

  2. Digital Screening: NemoClaw filters candidates through ProteinMPNN (solubility) and AlphaFold 3 (binding affinity).

  3. Robotic Execution: NemoClaw sends a "Synthesis & Assay" command to the lab’s Hamilton Venus API. The robot synthesizes the DNA and performs a $K_D$ binding assay.

  4. Telemetry Ingestion: The lab sensors upload raw data (Surface Plasmon Resonance curves) to an S3 bucket.

  5. Recursive Learning (RSI): NemoClaw parses the assay results. If the $K_D$ is too high (>100nM), it triggers a "Refinement Run" in EvoDiff, specifically targeting the failed motifs for mutation.

4. Implementation Roadmap for Startups

  • Week 1-2 (Integration): Deploy NVIDIA NemoClaw in a private Sovereign Cloud. Connect EvoDiff NIMs.

  • Week 3-4 (API Mapping): Map the laboratory’s robotic control software (e.g., HighRes Biosolutions) to NemoClaw "Tools."

  • Week 5 (The Pilot): Run a "Self-Correcting" 48-hour cycle for a non-therapeutic test protein.

  • Week 6+ (Scale): Parallelize across multiple robotic nodes for million-scale screening.

5. Strategic Value for TCG

By adopting this NemoClaw-centered architecture, your startups reduce Discovery Latency by an estimated 80%. They move from "Experimentalists" to "Architects of the Loop," a core requirement for reaching the Super Intelligence (ASI) milestones detailed in your roadmap.

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