AI-driven protein modeling risks deepening biotech monopolies while bypassing Indigenous medicinal traditions and ethical oversight
Original framing: “AI structure prediction speeds discovery of 'molecular glues' to treat disease” — Phys.org
The voices of autoimmune patients—disproportionately women, racialized groups, and the poor—are absent from this narrative, despite their communities bearing the brunt of both disease and exploitative 'cures.' For example, lupus patients in the Global South often lack access to the molecular glues being developed, while their traditional remedies (e.g., *neem* in India, *damiana* in Latin America) are sidelined. Marginalized scientists, like Dr. Chikwe Ihekweazu (former Nigeria CDC director), have criticized how AI in medicine replicates colonial power structures by centering Western institutions while excluding Global South expertise. The Baylor team’s lack of engagement with patient advocacy groups (e.g., the Lupus Foundation of America’s global chapters) further marginalizes those who would benefit least from these discoveries.
The Baylor-led AI research reflects a **techno-industrial paradigm** that treats biology as a computational puzzle to be solved, while ignoring the **relational, ecological, and ethical dimensions** of healing.
This framing is not neutral but a continuation of **biocolonial extraction**, where Indigenous knowledge and marginalized communities are the uncredited labor behind 'discoveries' that consolidate power in Western institutions and pharmaceutical corporations. The omission of cross-cultural systems—like TCM’s *yin-yang* balance or Ayurveda’s *rasayana*—reveals a deeper crisis: **modern medicine’s inability to integrate wisdom that predates its own origins**. The trickster dimension exposes the irony of AI as a 'liberator' when it actually **locks in monopolies** and deepens inequities, much like the historical role of patented 'cures' derived from stolen knowledge. To move forward, solutions must center **Indigenous data sovereignty**, **hybrid AI-traditional medicine models**, and **systems-level AI** that addresses the social-ecological roots of disease—not just its biochemical symptoms. The path forward lies in **decolonizing innovation**, where AI becomes a tool for **restoration** rather than extraction.