Author: Darssheela Ramasamy, MSc (Biotechnology Professional)
Date: 3 November 2025
For decades, science had relied on curiosity, observation and persistence. From the invention of antibiotics to the mapping of the human genome, progress has always depended on how quickly humans could observe, analyse, interpret and test the world around them. However, in today’s era, where billions of data points are generated every second, human capacity alone is no longer enough. This is where artificial intelligence (AI) comes in, emerging as a new method of invention to reshaping the future of drug discovery and scientific research.
From Discovery Fatigue to Digital Innovation
The gradual increase in the number of published papers and patents has made it increasingly difficult for researchers to generate truly original ideas. Many faces a knowledge burden simply because there is too much information for the human brain to digest.
AI, particularly neural networks (NNs), helps overcome this challenge. A neural network is a computer system that recognises patterns and makes decision much like the human brain. It processes information, through many connected layers known as neurons, and produces an output [1].
These intelligent systems achieve at spotting loopholes in vast datasets, revealing relationships that could take human scientist years to notice. Rather than replacing scientist, AI acts as a capable research partner that could help navigate this complex web of modern science.
The Double-Boom of Artificial Intelligence
Neural networks first appeared decades ago but truly boomed in the 2010s, when computing power and data availability finally caught up with theory. According to a research article, AI has spread rapidly across nearly all fields of science starting from physics, chemistry and now to medicine and social science [2].
Developed countries like the United States, Europe and China now dominate AI-based research, which has doubled or tripled in many disciplines within a single decade. AI has become an emerging general method of invention which means it isn’t confined to a single domain rather it can be applied to almost any field that require discovery and creativity. The unique strength of AI here is its ability to search existing knowledge efficiently and discover combinations that leads to innovation.
When AI is Used in Drug Discovery
Imagine a team of researchers looking for a new potential drug for influenza or perhaps for Alzheimer’s disease. Traditionally, it takes way longer time to analyse the known molecules, running countless laboratory tests and hoping for a breakthrough which is super time-consuming and costly.
Now, just imagine if AI is introduced into this equation. A neural network trained on millions of research papers, chemical structures and biological pathways can just surf through vast molecular libraries within minutes. It can predict which plant derived compounds or synthetic compounds share structural similarities with existing drugs on the market.
AI is not merely a tool for storing data but it recombines knowledge from multiple disciplines. It might link a molecule studied in herbal medicine with a receptor target discovered in virology, suggesting a potential new treatment pathway. Scientist can test these AI-generated hypotheses in the lab. When experiments confirm the predictions, a new antiviral compound is born, which is not purely from human intuition but from human-AI collaboration.
According to several analysis, AI is already transforming health sciences. Research papers using neural networks are cited more often, indicating a higher scientific impact. However, most studies tend to build upon existing knowledge rather than creating entirely new fields. In simpler terms, researchers use AI to dig deeper into known areas instead of exploring uncharted territory.
This is understandable: emerging technologies often start as support tools before becoming engines of true innovation. As scientists gain confidence and interpretability improves, AI will likely drive more cross-disciplinary breakthroughs bridging medicine, chemistry, and biotechnology in unprecedented ways.
Why This Matters
AI may change not only what scientists discover but also how they discover it. From molecule designing and toxicity prediction to suggesting synthetic routes and even drafting sections of research paper. AI’s capabilities are remarkable. Some systems can autonomously plan experiments and adjust parameters in real time based on results. These types of self-learning features make AI an invaluable companion in the race to develop potential treatments for complex disease, from viral infections to cancer.
A New Era of Discover
The future of science is not human vs machine but it’s human with machine. AI expands scientist’s capacity to think bigger and test hypotheses quicker than ever before. It can help a biologist uncover a new bioactive compound, a chemist design sustainable material, or a doctor to personalise treatments for each patient.
In short, AI is evolving from analytical instrument to a creative partner, one that doesn’t just help us find the answers but also asks entirely new questions. As we continue this journey, AI may very well become the most powerful scientist we have ever created, guiding humanity through the next great wave of scientific invention.
References
- Cockburn, I.M., Henderson, R., Stern, S., 2018. The Impact of Artificial Intelligence on Innovation (No. w24449). National Bureau of Economic Research. https://doi.org/10.3386/w24449
- Bianchini , Müller, Pelletier ,2022. Artificial intelligence in science: An emerging general method of invention. Research policy. https://doi.org/10.1016/j.respol.2022.104604
Scientect 