AI accelerates drug discovery by mapping protein interactions, yet systemic gaps in data diversity and ethical oversight persist

Indigenous knowledge systems, such as those in Ayurveda and TCM, have long recognized the interconnectedness of protein interactions within holistic health frameworks, yet these are systematically excluded from AI training datasets. The commodification of genetic data from indigenous communities without consent or benefit-sharing exemplifies extractive research practices that AI-driven drug discovery may perpetuate. Indigenous scholars and healers have proposed alternative models of protein interaction that prioritize ecological and communal well-being over individual profit.

The history of protein research is marred by colonial-era exploitation, such as the Tuskegee Syphilis Study and the Henrietta Lacks case, where marginalized communities were subjected to unethical experimentation. The Human Genome Project further exposed the inequities in global biomedical research, with 96% of genetic data originating from individuals of European descent. This historical context underscores the risks of AI models trained on biased datasets, which could reproduce past injustices in new technological forms.

Cross-culturally, protein interactions are framed differently: Western biomedicine isolates molecular pathways, while systems like TCM view them as part of a dynamic energy network (e.g., 'Qi' or 'Prana'). African ethnobotanical traditions, such as those in the Congo Basin, have identified plant compounds that modulate protein interactions without the need for synthetic drugs. Integrating these perspectives could democratize drug discovery and reduce reliance on patented pharmaceuticals.

The AI model’s predictive accuracy is grounded in deep learning techniques trained on protein-protein interaction (PPI) databases like STRING and BioGRID, which are largely derived from Western biomedical research. While the technology shows promise in reducing trial-and-error in drug design, its reliance on curated datasets risks reinforcing existing biases. Peer-reviewed studies highlight the need for diverse, high-quality datasets to improve generalizability, yet such efforts remain underfunded compared to proprietary AI development.

Artistic and spiritual traditions often depict protein interactions as metaphors for interconnectedness, such as the 'web of life' in Indigenous cosmologies or the 'holy grail' of alchemy in Western esotericism. These frameworks challenge the reductionist paradigm of modern science, suggesting that protein interactions are not merely biochemical but part of a larger existential narrative. Creative disciplines, such as bioart, have begun exploring these intersections, though their insights are rarely integrated into mainstream scientific discourse.

Future scenarios for AI-driven drug discovery include both utopian and dystopian outcomes: accelerated cures for rare diseases versus monopolized access and ecological harm from industrial-scale production. The model’s scalability could either democratize medicine or exacerbate global health disparities, depending on governance frameworks. Scenario planning must account for climate change, which disrupts supply chains for pharmaceutical ingredients, and the potential for AI to optimize drug delivery systems in resource-constrained settings.

Marginalized communities, including Black, Indigenous, and low-income populations, are underrepresented in both the datasets and the decision-making processes shaping AI drug discovery. Their exclusion risks perpetuating health inequities, as drugs developed without diverse input may fail to address the needs of these groups. Grassroots organizations, such as the Global Indigenous Data Alliance, advocate for data sovereignty and ethical frameworks to ensure equitable participation and benefit-sharing.