University of Waterloo researchers are using machine learning and interdisciplinary expertise to speed up drug development and advance personalised medicine by improving how pharmaceutical data is analysed and applied.
Drug development is widely recognised as a complex, time-consuming and costly process, often requiring billions of dollars and many years of research, with no guarantee of success. Against this backdrop, an interdisciplinary research team at the University of Waterloo is leveraging machine learning to significantly accelerate pharmaceutical research and reduce inefficiencies caused by trial-and-error approaches.
Also Read: ‘Pulsed field ablation is emerging as part of standard AFib care in the region’
The team is developing advanced machine learning models capable of analysing and synthesising vast volumes of pharmaceutical data to predict drug properties and interactions. “We have a lot of existing data across a broad spectrum of medical domains, but it’s extremely complex, and often not as complete or extensive as we would like,” said Dr. Helen Chen, professor of practice in Public Health Sciences. “It’s like a very shallow ocean.”
Dr. Chen is working alongside Bing Hu, a PhD candidate in Computer Science, to design models that can better interpret how drugs behave in the human body. To strengthen the biological accuracy of the system, the team collaborated with Dr. Anita Layton, professor of Applied Mathematics, who is internationally recognised for her work on mathematical models of kidney function.
“Often, when we use machine learning to train neural networks, we’re starting from scratch,” Hu said. “But by drawing on the enormous amount of domain specific knowledge coming from biology and medicine, we’re able to build more efficient, more accurate models whose predictions consistently match-up with existing data from the real world.”
The model developed by the researchers can predict how drugs interact with specific protein targets and assess potential efficacy and safety outcomes in the body. According to Chen, “Personalized treatment is the next frontier in medicine. Machine learning research like this is putting that treatment in the hands of everyone.”
The initiative extends beyond the University of Waterloo, with the research team collaborating internationally to enhance data quality, test hypotheses and improve the efficiency of laboratory and clinical trials. In Ontario, they are working with clinicians at Princess Margaret Cancer Centre, while globally they are collaborating with the Advanced Data Science Lab at Yonsei University in South Korea to explore broader applications and impact.
“AI is powerful and exciting, but we need to focus on using it to build tools that will actually benefit people,” Hu said. “That development needs to be a collaborative process where you work with experts to create the tools they need to make the next world-changing breakthrough.”
Click here to join our WhatsApp channel here
“One of the most exciting things about this work is that we’re bringing together perspectives from so many disciplines,” Chen added. “That convergence, combined with the power of AI, makes discovery so much faster. It’s like before we were riding a horse from A to B, and now we’re riding in high-speed trains.”
