By Vishnu Narayan For centuries, scientific discovery has been regarded as one of humanity’s most individual pursuits. We often associate breakthroughs with remarkable people: Newton beneath an apple tree, Pasteur in his laboratory, Marie Curie in pursuit of radioactivity, or Tu Youyou’s pioneering work on artemisinin.
While science has always depended upon collaboration, the act of discovery itself has remained fundamentally human. Instruments extended observation. Computers accelerated calculation. Laboratories generated evidence. Scientists remained responsible for asking the questions.
“Science is organised knowledge.” – Herbert Spencer
That assumption is beginning to change.
Across pharmaceutical research, artificial intelligence is increasingly contributing not only to analysing data, but also to identifying biological targets, proposing molecular structures, predicting toxicity, designing experiments, and refining hypotheses through continuous feedback.
Large pharmaceutical companies and emerging biotechnology firms alike are integrating AI throughout the research and development process, transforming workflows that until recently depended almost entirely on human expertise.
The significance of these developments lies not simply in speed.
They suggest that scientific discovery is becoming progressively collaborative, where knowledge is produced through interactions between researchers, computational systems, decades of accumulated scientific data, and increasingly sophisticated institutional infrastructures.
This represents a subtle but important shift. The question is no longer whether AI can assist scientific research. Increasingly, it is how scientific institutions should govern knowledge that is co-produced by humans and machines.
From scientific instruments to scientific partners
Every generation has witnessed technologies that transformed the practice of science.
The microscope revealed worlds invisible to the naked eye. Genome sequencing accelerated our understanding of biology. High-performance computing reshaped climate modelling, astrophysics, and molecular chemistry. None of these technologies altered who was considered responsible for scientific reasoning.
Today’s AI systems are different.
Recent developments demonstrate systems capable of identifying novel therapeutic targets by analysing genetic data at scales previously impossible, generating millions of candidate molecules before laboratory validation, integrating decades of clinical evidence to predict safety, and connecting scientific observations that might otherwise remain isolated across disciplines.
Scientists remain central to discovery. Yet increasingly, they validate hypotheses that computational systems have helped generate. Scientific reasoning itself is becoming distributed.
A familiar public policy problem
In many respects, this resembles a classic public policy challenge. Technological capability often advances more rapidly than institutional adaptation.
The Industrial Revolution transformed manufacturing long before labour protections emerged. The internet reshaped communication before societies developed meaningful approaches to digital governance. Financial innovation repeatedly outpaced regulatory oversight until crises forced institutional reform.
Scientific discovery now appears to be entering a similar period. Regulatory policies remain primarily concerned with scientific outputs. Medicines undergo rigorous clinical trials. Medical devices are evaluated for safety and effectiveness. Regulatory agencies assess evidence before approving products for public use.
Far less attention has been devoted to how scientific knowledge itself is increasingly generated. If AI proposes a promising therapeutic target, who is ultimately responsible for validating it? How should institutions assess evidence generated through computational reasoning?
What standards of transparency, reproducibility, and scientific independence remain necessary when hypotheses emerge from systems whose internal reasoning may not always be fully interpretable?
Imagine an AI recommends studying a particular gene linked to Alzheimer’s disease. Experiments later confirm the discovery. While the outcome may be correct, scientists and regulators may still ask an important question: why did the system choose that gene and not hundreds of others? Science has long relied not only on correct answers, but on understanding how those answers were reached.
These are not arguments against AI. They are questions about institutional responsibility.
“Knowledge is the only means of production that is not subject to diminishing returns.” – Peter Drucker
Scientific independence matters even more
Public trust in medicine has never depended solely upon scientific excellence.
It has depended equally upon confidence that evidence has been generated through transparent, reproducible, and accountable processes. That principle does not disappear as AI becomes more capable.
Indeed, it becomes more important. As computational systems contribute more substantially to scientific reasoning, human oversight increasingly shifts from generating every hypothesis to evaluating which hypotheses deserve confidence. Peer review, replication, independent validation, and regulatory scrutiny become stronger rather than weaker pillars of scientific governance.
The role of scientists evolves.
The responsibility of scientific institutions expands.
A global opportunity
There is another dimension that deserves greater attention.
Much of today’s discussion around AI-enabled discovery understandably focuses on major pharmaceutical companies and advanced research centres. Yet some of the greatest societal benefits may emerge elsewhere.
For many low- and middle-income countries and Small Island Developing States, limited research infrastructure has historically constrained participation in pharmaceutical innovation.
AI has the potential to lower some of these barriers by expanding access to computational discovery, enabling researchers to analyse complex biological data, identify locally relevant disease priorities, and participate more meaningfully in international scientific collaboration. Realising that opportunity, however, will depend upon more than computational capability alone.
It will require investment in research capacity, equitable access to high-quality scientific data, trusted international partnerships, and governance frameworks that allow countries to contribute knowledge rather than simply consume technologies developed elsewhere.
Scientific collaboration should not reinforce existing inequalities. It should broaden participation in discovery itself.
Measuring progress differently
Success in pharmaceutical innovation has traditionally been measured through the number of compounds identified, clinical trials completed, or medicines approved. These indicators remain important.
Yet the coming decade may require an additional measure.
Can societies build scientific institutions capable of governing increasingly collaborative forms of discovery while preserving public trust, scientific independence, and equitable participation? That challenge extends well beyond pharmaceutical research. It concerns how knowledge itself is produced.
Artificial intelligence will undoubtedly become an increasingly important contributor to scientific discovery. The more enduring question is whether our institutions evolve alongside it. The age of collaborative science should not be defined by how much reasoning machines can perform.
It should be defined by how effectively societies ensure that scientific discovery remains transparent, accountable, inclusive, and directed towards the public good. If history offers any lesson, it is that scientific progress has always depended as much on the strength of institutions as on the brilliance of inventions.
The next chapter of discovery may prove no different.
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Matthew Brady