AI-Designed Proteins for Snake Venom Neutralization

1. Biological Engineering Application

Application: AI-driven de novo protein design for neutralizing snake venom toxins. (paper)

Why: This study successfully used modern computational tools (AlphaFold2, ProteinMPNN, RFDiffusion, molecular dynamics simulations) to create synthetic proteins that bind and neutralize snake venom toxins. This method is novel because it eliminates the need for traditional animal-derived anti-venoms, allowing for faster, scalable, personal, and more effective solutions to snake envenomation. The goal of this is to create a more efficient system of developing proteins in smaller markets around the world, much like Sam Walton and the original Walmart locations.

2. Governance & Policy Goals

To ensure an ethical and responsible deployment of AI-designed therapeutic proteins, the following governance goals should be prioritized:

1. Biosecurity & Ethical Deployment: Ensure that AI-generated proteins cannot be repurposed for harmful applications, embedded in the actual pipeline.
2. Regulatory Approval: Establish guidelines for AI-generated therapeutics to be rigorously tested and approved by agencies like the FDA, adhering to laws and practices like IND, BLA and Public Health Service Act (PHSA) – 42 U.S.C. § 262, specifically for genetic engineering.
3. Accessibility: Ensure that AI-designed anti-venoms remain affordable and accessible, especially in high-risk regions like Africa and South Asia.

3. Governance Actions & Considerations

Action 1: Implement AI Safety & Biosecurity Measures

• Purpose: AI-generated proteins can be repurposed for harmful applications. We can add invariants in the process that prevent specific negative actions from happening, like harming other humans and the environment. This needs to be embedded in the workflow to lower cost.
• Design: Require labs using AI-designed proteins to register their research and follow strict biosecurity protocols.
• Assumptions: AI models may not always predict unintended consequences, requiring oversight.
• Risks of Failure & Success:
• Failure: AI-designed proteins could be misused or lead to unexpected biological interactions.
• Success: Strong regulations prevent misuse while still enabling research progress.

Action 2: Standardizing AI-Generated Therapeutics for FDA Approval

• Purpose: AI-designed proteins require a new regulatory framework to ensure safety and efficacy.
• Design: Develop a validation pipeline combining AI simulations, wet lab testing, and clinical trials. All of these automated tests have legislation actually encoded in the workflow.
• Assumptions: Current approval processes (designed for traditional drugs) may not be well-suited for AI-designed molecules.
• Risks of Failure & Success:
• Failure: Delayed approval timelines due to lack of regulatory clarity.
• Success: AI-generated therapies become mainstream with defined safety standards.

Action 3: Establishing Affordable Manufacturing & Distribution Pipelines

• Purpose: Ensure that AI-designed anti-venoms are manufactured at scale and distributed affordably.
• Design: Invest in low-cost, scalable protein production methods (e.g., yeast/bacterial expression systems, or cell-free synthesis if feasible in the future).
• Assumptions: Current protein production may still be too costly for mass deployment.
• Risks of Failure & Success:
• Failure: High costs prevent widespread adoption, limiting real-world impact.
• Success: Scalable, cost-effective production saves lives in high-risk regions.

4. Scoring Governance Actions

5. Prioritization & Trade-offs

Option 3 (Affordable Manufacturing & Distribution) is the highest priority, however, it is dependent on success of Option 1 and 2. Efficacy and impact need to be an automated part of the design, much like OpenAI and other foundation models with training their models (via RLFH)

Trade-offs & Assumptions:

• Local regulatory oversight may slow innovation, requiring flexible but rigorous approval mechanisms.
• Affordability could be challenging initially, necessitating public-private partnerships to fund manufacturing scale-up.
• Ensuring safety without stifling AI-driven innovation requires balance between oversight and scientific freedom.

Relevant Audiences:

• National Regulatory Agencies (FDA, EMA): Establish AI-driven therapeutic approval pathways.
• Global Health Organizations (WHO, Gates Foundation): Support affordable distribution in developing regions, like the Gates Foundation and other non-profits with direct distribution into difficult to access and low ROI market regions.
• Industry (Biotech Companies, Startups): Collaborate on scaling manufacturing and ensuring ethical AI use.

Final Takeaway

This study highlights a new era of AI-driven protein design, but its real-world success depends on safe, regulated, and scalable deployment. We prevent misuse by embedding a QA system like those used by foundation models, ensuring safety, and making life-saving treatments accessible worldwide. Distribution will require its own strategy, which will eventually become an extension of the pipeline itself.