Who Controls the Cure? AI and the Future of Medicine

BY MIRIAM SIROKY

What if a life-saving drug could be designed in weeks instead of decades? For most of modern history, attempts to develop a single new medicine have taken over ten years and cost billions of dollars without guarantees of success. Scientists test millions of chemical compounds in laboratories, eliminating dangerous and ineffective formulas until, in rare cases, one viable candidate remains. This slow, expensive process leaves many diseases untreated because the financial risk of failure is too high. Artificial intelligence (AI) is disrupting this model.

Using advanced algorithms to identify, design, and optimize drug molecules enables researchers to compress development timelines from years to months or even weeks. AI-designed drugs are not just transforming medicine; they are influencing the economics and politics of entire nations by redefining who controls drug development, how countries respond to health crises, and what determines access to new life-saving treatments.

The traditional drug development process contains five major stages: discovery and development, preclinical research, clinical trials, regulatory review, and post-market monitoring. The first phase is usually the most expensive and least certain. Researchers identify a biological target, often a protein linked to a disease, and then search for a molecule capable of interacting with it. The protein’s structure matters because shape determines function: if scientists understand a protein’s binding sites, they can design drugs to address them. Trial failure rates typically exceed 90%. Even when a drug candidate is identified, it may take years for researchers to determine whether it is safe and effective enough to be used as a treatment.

Artificial intelligence is transforming the discovery and development stage. By virtually screening millions of compounds for their ability to bind target proteins, their potential toxicity, and their metabolic clearance, AI is proving that it can eliminate weak contenders before expensive laboratory testing even begins.

Take AlphaFold, an AI developed by DeepMind (a subsidiary of Alphabet, Inc.). The AI recently mapped the three-dimensional structure of proteins. This task, which previously took scientists years to conduct with X-ray crystallography, now requires only a few hours and a computer. AlphaFold has already predicted structures for more than 200 million proteins, providing researchers with the structural information needed to target proteins previously considered ‘undruggable’.

Beyond protein prediction, generative AI models are now being deployed to design drug molecules themselves. Companies such as Insilico Medicine and Exscientia are generating unique chemical configurations optimized for specific issues. See, for instance, Exscientia’s AI-designed drug, DSP-1181. Intended to treat obsessive-compulsive disorder, it was the first AI-generated molecule to enter clinical trials. Its design phase took less than twelve months, compared to the industry average of four to five years.

Additionally, the PandaOmics and Chemistry42 AI models identified a novel treatment for idiopathic pulmonary fibrosis (a chronic, progressive lung disease) in just 46 days, marking the first time that AI both captured the protein and generated a new drug structure. The drug quickly entered Phase II clinical trials, demonstrating that this technology can move compounds from concept to human testing at unprecedented speed. Researchers reported in late 2025 that this drug has indeed been linked to improved lung function and better quality-of-life scores for cough and respiratory symptoms.

The economic implications of this shift to AI drug development are substantial. Faster discovery reduces research costs by minimizing failed experiments and lowering the financial risk of innovation. AI implementation could also reduce drug prices, particularly for rare disease treatments that were considered unprofitable to study.

AI-driven drug development also has major policy implications. Countries with advanced AI infrastructure can respond rapidly to public health emergencies. During the COVID-19 pandemic, researchers used AI to identify antiviruses and optimize vaccine design. Moderna modeled its mRNA vaccine with AI, achieving record-breaking development speed. Nations with AI-enabled drug pipelines will gain strategic advantages in outbreak containment, public trust, and global influence.

These advancements may also deepen global inequality. As political scientist Harold Lasswell famously argued, “Politics is about who gets what, when, and how.” In the context of AI-driven drug development, wealthier nations build the necessary infrastructure to access vast datasets, cloud computing resources, and advanced algorithms. This concentration determines not only who can innovate, but who gains access to life-saving treatments.

Jewish law issues a warning against such imbalances. The Torah commands, “Justice, justice shall you pursue” (Deuteronomy 16:20), which is not only a legal principle but also a demand for fairness. Mastery of AI-designed medicine entails control over global health outcomes through pricing, licensing, and distribution; this technological revolution imparts both economic and moral imperatives.

In the Torah, healing does not defy God; rather, it is a moral responsibility. The verse “ורפא ירפא”— “and He shall surely heal” (Exodus 21:19) requires humans to practice medicine. AI-driven drug development is a modern application of this mandate. Furthermore, pikuach nefesh, the obligation to save lives, indicates that developing life-saving drugs is not just innovative but morally urgent.

AI-designed drugs represent more than a scientific breakthrough; they mark a seismic shift in medicine, economics, and politics. The technology promises faster cures, lower costs, and more precise treatments, but the question of how its benefits will be distributed remains unresolved. As AI continues to redefine drug development, the central question is no longer what AI can do, but who controls it, who regulates it, and who ultimately benefits from its cures.