In this article, Dr Alan D Roth, CEO of Oxford Drug Design, explains how AI is transforming future medical treatments for major diseases and illnesses such as cancer and cystic fibrosis, how and why we will see advances in Machine Learning grow this year, and how better treatments for these diseases and illnesses are closer than we think.
Two years ago, the global AI in healthcare market was valued at US$16.3 billion, with an expectation that it would reach US$173.6 billion by 2029. It’s a market that is growing at a remarkable pace thanks to its ability to be used in various parts of the drug discovery process. The power of this technology is something that the pharmaceutical and biomedical industry is already harnessing to discover drugs with greater speed and reduced costs.
We can expect strong advances in 2025 also in AI’s ability to discover cancer drugs with greater precision than ever seen before and this can lead to improving outcomes for investors, practitioners and most importantly for patients.
How is AI accelerating drug discovery?
AI-driven drug discovery is evolving rapidly, encompassing the many stages throughout the entire drug development process from the very beginning of designing a novel therapeutic to its eventual approval for patient use.
Our expertise at Oxford Drug Design lies in the earliest stages of this process. This is when a specific molecule – a chemical compound with the potential to become a therapeutic drug – is designed, discovered and developed before it reaches a clinical setting.
The computational capability of our platform, Synth AI, is a powerful multitool approach to designing novel drugs with remarkable accuracy. This pioneering, validated technology is completely disease-agnostic, meaning it can be applied across any therapeutic area.
We’ve already demonstrated its versatility in our own pipeline, particularly in oncology and infectious diseases. However, this capability has been proven in the drug discovery programs of other pharmaceutical and biotech companies across numerous other diseases thus demonstrating its broad applicability. Another key advantage of a highly versatile AI drug discovery platforms such as ours is that it can be used to find new molecules against a wide variety of targets.
Modern drug discovery is largely driven by the selection of such a disease target, the body’s biochemicals that malfunction leading to disease. Computational technologies available to the drug discovery industry today can often work for numerous targets, regardless of the disease or the nature of the target itself. Prior to the availability of computational methods, the process of identifying a target and then designing a molecule against using conventional means was significantly slower and less accurate.
Our platform Synth AI, for example, helps preclinical drug discoverers find innovative molecules fulfilling three key metrics: they possess the required biological activity against disease, can be made using known chemical processes, and can be produced at scale. This approach means that treatments are developed with greater accuracy and are therefore much more likely to achieve validation at every stage of preclinical and clinical trials. Drugs coming to market after such an accelerated process are more cost-effective to produce, making the space highly attractive for investors while improving outcomes for physicians and patients.
Overall, these new AI-enabled drugs stand to be of superior efficacy and safety when replacing older drugs with limitations in both criteria. AI truly has the potential to revolutionise global medical and specifically therapeutic care.
The future of AI in the clinic
Looking to the future of AI, one of the key features of our approach is its increasing flexibility.
Using SynthAI one can start designing a new drug based on a specific target, while in other contexts the search begins with an active molecule and then one seeks to discover what target it could be most effective for. Today’s SynthAI technology can approach the discovery process from either direction, and we’ve proven that it works effectively in both scenarios, in particular with our own work on oncology and resistant infections.
At Oxford Drug Design we are currently focused on oncology, working against various types of tumours where our drug candidates have already shown promising results in animal models. Our expertise also extends to important and lethal infections, including those related to Cystic Fibrosis.
As we enter the next stage of our growth, we are conducting an important fundraising exercise and plan to expand our AI-enabled pipeline to address even more diseases with unmet need using our advanced computational and biochemical expertise. These may include major current areas of medical interest such as healthy ageing and further specific applications in oncology where it has been difficult to find effective treatments.
The key though is that an innovative drug discovery company like Oxford Drug Design can deploy its distinctive biochemical target expertise to accompany our computational capability thus enabling our expansion into seemingly unrelated disease areas which in our hands do share a common thread. In our work this thread is the world-class understanding of tRNA synthetases, an enzyme family on which Oxford Drug Design has developed a deep understanding, and which can connect diseases including inflammation, degenerative kidney disorders and others.
While it’s difficult to accurately predict how many years AI will ultimately reduce the time it takes to develop a drug or any medical approach, one thing is certain: the overall process of reaching effective, safer drugs is gradually accelerating. In 2025, this heightened accuracy and speed of progression is what we expect to see.
