AI’s role in drug discovery is advancing quickly, and the future will revolve around using AI and machine learning to accelerate processes.
Recent reports indicate that artificial intelligence has the potential to reshape the pharmaceutical industry and that it is already creating tangible change along the entire value chain.
The global market for AI drug discovery is set to rise from around $1.5 billion now to approximately $13 billion by 2032, while investment in AI-based solutions in clinical research is forecast to increase to more than $7 billion by 2030.
These figures highlight the significant and growing role AI is playing in shaping the future of pharmaceutical development. But how exactly is it expediting the drug discovery process, not only at the pre-clinical stage, but across the entire drug development cycle?
The impact of AI on drug development
AI-driven drug discovery is evolving at pace and transforming every stage of the development process, from the initial design of a new therapeutic to final approval and market use for patients and their physicians after successful clinical trials.
A great example of AI’s impact on drug discovery focuses on the earliest phases of this process, namely when a potential molecule, the chemical compound, is first designed and developed. I’ve observed this approach accurately find novel drugs.
In addition, I have observed this approach to be successful pre-clinical in vivo validations for a new cancer treatment, and it also has validated therapeutic candidates targeting resistant uUTI and cystic fibrosis infections. Beyond oncology applications, the approach also shows promise in other areas, such as infectious diseases. This approach is disease-agnostic and can be applied across any therapeutic area. Thus, external biotechnology and pharmaceutical companies internationally are using it to address discovery challenges.
What makes AI drug discovery so powerful is that it can source new drug candidates based on specific criteria. AI can search for innovative molecules that fulfill multiple key metrics simultaneously – for example, whether molecules possess the required biological activity against disease, can be made in the lab using known chemical processes and can be produced at scale so as to ensure financial viability.
Approaching drug discovery this way means that any new treatments start their journey at a more advanced point along the development progression.
As a result, these drugs are more likely to achieve validation at subsequent stages of preclinical and clinical trials, speeding up discovery, development and eventual administration of treatments. With drugs coming to market more quickly, the outcomes for investors, practitioners and principally patients are improved.
As pharmaceutical companies use AI in drug discovery, they are able to more quickly advance solutions into their own pipelines. The impact that AI is having on drug discovery right now cannot be overstated.
Using AI in the clinic
Indeed, AI is so effective that it isn’t just enhancing drug discovery. Once drugs reach clinical trials in humans, there are multiple ways the technology can assist in this final stage of development and regulatory approval.
When patients participating in a trial are given the drug or placebo, AI can be used to track all relevant data points relating to their health at levels of detail and connectivity never before possible.
After a drug is administered, AI can simultaneously analyze all vital signs, including heart rate, temperature, metabolism and more, and then better pattern any changes to these critical metrics. This can alert physicians to any concerning shifts and immediate risks, but it also can create analytical data reports that track these changes over time, providing researchers with the crucial insight they need to make a decision on the drug’s safety for human use.
Researching scientists, physicians and drug data administrators can use the new technology to enhance their respective work and then integrate it all in novel ways, thus saving time, improving accuracy and mitigating the risk of human error. AI can specially detect patterns and links between different parameters that would be difficult to discover using only clerical or observational means.
Furthermore, reducing the time it takes to develop drugs at these later stages saves investors and organizations money, freeing up these funds to be invested into other critical projects.
In an industry that relies on significant early-stage investment to develop treatments, the ability to create compounds that reach patients more quickly and cost effectively is extremely important.
Overall, AI is changing for the better the way in which millions work every day around the world, and the pharma industry is a prime example of this. That’s because AI and machine learning enhancements are playing an increasingly vital role in helping brilliant scientific and medical minds uncover the pathways to discovery.
It’s impossible at this stage to predict exactly how much time and investment AI will save within the drug development industry, but it’s clear that the journey towards creating more effective, safer and more readily available drugs is being accelerated by this technological advancement.
Dr. Alan D. Roth is CEO of Oxford Drug Design.
