The role of AI in drug discovery is rapidly evolving, and the future will revolve around using AI and machine learning to accelerate the process.
Recent reports show that artificial intelligence has the potential to reshape the pharmaceutical industry and is already driving measurable changes across the value chain.
The global market for AI drug discovery is expected to grow from approximately $1.5 billion today to approximately $13 billion by 2032, and investment in AI-based solutions in clinical research is expected to increase to more than $7 billion by 2030.
These numbers highlight the important and growing role that AI will play in shaping the future of drug development. But how do you speed up the drug discovery process, not just at the preclinical stage, but throughout the drug discovery cycle?
Impact of AI on drug development
AI-powered drug discovery is rapidly evolving and transforming every step of the development process, from the initial design of new therapeutics to final approval and market use for patients and physicians after successful clinical trials.
A great example of the impact of AI on drug discovery focuses on the early stages of this process, when potential molecules, compounds, are first designed and developed. I have observed that this approach accurately discovers new drugs.
Additionally, I have observed this approach to be successful in preclinical in vivo validation of new cancer treatments, as well as therapeutic candidates targeting resistant uUTI and cystic fibrosis infections. Beyond oncology applications, this approach also holds promise in other fields such as infectious diseases. This approach is disease independent and can be applied to any therapeutic area. Therefore, external biotech and pharmaceutical companies are using it to address discovery challenges around the world.
What makes AI drug discovery so powerful is the ability to source new drug candidates based on specific criteria. AI can search for innovative molecules that meet multiple key metrics simultaneously. For example, whether the molecule has the necessary biological activity against the disease, whether it can be manufactured in the laboratory using known chemical processes, and whether it can be produced on a large scale to ensure economic viability.
Approaching drug discovery in this way means that new treatments are initiated at a later point in the development process.
As a result, these drugs are more likely to achieve validation in subsequent stages of preclinical and clinical trials, speeding discovery, development, and eventual administration of therapy. Bringing medicines to market faster improves outcomes for investors, healthcare professionals, and primarily patients.
Pharmaceutical companies use AI for drug discovery so they can advance solutions into their pipelines more quickly. The impact that AI is currently having on drug discovery cannot be overstated.
Utilizing AI in the clinic
In fact, AI is so effective that it can do more than just enhance drug discovery. Once a drug reaches human clinical trials, there are multiple ways in which technology can assist in the final stages of development and regulatory approval.
Once a drug or placebo is administered to a patient participating in a clinical trial, AI can be used to track all relevant data points about the patient’s health with a level of detail and connectivity never before possible.
After a drug is administered, AI can simultaneously analyze all vital signs, including heart rate, body temperature, metabolism, etc., and better pattern changes in these important metrics. This can alert doctors to concerning changes or impending risks, but it can also generate analytical data reports that track these changes over time, giving researchers the critical insights they need to determine a drug’s safety for human use.
Research scientists, physicians, and pharmaceutical data managers can use new technologies to enhance their work and integrate everything in new ways to save time, improve accuracy, and reduce the risk of human error. AI is uniquely able to detect patterns and links between different parameters that are difficult to discover through clerical or observational means alone.
Additionally, reducing the time it takes to develop these late-stage drugs saves investors and organizations money that can be invested in other important projects.
In an industry that relies on large early-stage investments to develop treatments, the ability to create compounds that can be delivered to patients more quickly and cost-effectively is critical.
Overall, AI is changing the way millions of people around the world work every day for the better, and the pharmaceutical industry is a great example of this. That’s because advances in AI and machine learning are playing an increasingly important role in helping leading scientists and medical scientists uncover paths to discovery.
At this stage, it is impossible to predict exactly how much time and investment AI will save the drug development industry, but it is clear that advances in this technology are accelerating efforts to develop more effective, safer, and more readily available medicines.
Dr. Alan D. Ross is the CEO of Oxford Drug Design.
