Researchers from Simon Fraser University (SFU) have unveiled a groundbreaking artificial intelligence (AI) framework that promises to transform drug development and accelerate the discovery of new medicines. This innovative approach addresses a longstanding challenge in the pharmaceutical industry: designing and manufacturing effective drug molecules.
In what could be a significant breakthrough for healthcare, the study introduces a method that could dramatically reduce the time required to discover and produce drugs for common diseases, such as cancer. For years, AI tools have shown potential in designing complex molecular structures that theoretically interact with disease targets. However, many of these ‘perfect’ molecules are often impossible to manufacture in real-world laboratories.
Overcoming Challenges in Drug Development
Martin Ester, a professor of computing science at SFU, highlights the lengthy and costly process of drug development.
“The development of a new drug is an extremely time-consuming and expensive process. As a rule of thumb, people always say that it takes 10 years and $1 billion USD to bring a new drug to market. Our hope is that our method will significantly shorten this process so that new drugs can be discovered, produced, and made available in a much shorter timeframe in order to help cure diseases.”
One of the core challenges in AI drug design is the synthesis pathway—the ability to devise a realistic chemical recipe to construct the molecule. Without this, even the most promising AI-designed molecules are often discarded, leading to wasted time and resources.
The CGFlow Method
The new method, called CGFlow, introduces a dual-design approach that enables AI to simultaneously model how a molecule is constructed and what it looks like in 3D space. This combination is essential for generating molecules that are not only biologically effective but also chemically feasible to produce.
Ester elaborates on the significance of this development:
“We have developed a machine-learning method that practically guarantees that the molecule generated can be created through chemical synthesis in the real world. This is a hugely important aspect in translating the results of these generative models into practical applications, it is very exciting.”
Instead of designing molecules in one go, CGFlow assembles them step by step, akin to sculpting a statue by adding one piece of clay at a time. With each step, the AI learns how the new component changes the overall shape and function of the molecule, resulting in more accurate and efficient designs.
Implications for the Pharmaceutical Industry
The model’s potential is already being recognized beyond the lab. Several companies are considering adopting the CGFlow framework for early-stage cancer drug discovery, offering new hope in the fight against complex diseases.
Tony Shen, an SFU PhD student and lead author of the paper, explains the process:
“The fight against disease starts with identifying the disease-causing protein. In the lab, computer models are then used to design molecules that will bind to the disease-causing protein, often deactivating it and stopping its harmful activity. The whole process is a bit like trying to design a key that will fit into a lock.”
The next step for the researchers is to collaborate with industry partners to evaluate and further develop CGFlow in practical applications.
“We’re really interested in working with industry to evaluate and further develop CGFlow in practical applications,” adds Ester.
Future Prospects and Industry Collaboration
The study was published at the International Conference on Machine Learning 2025 in Vancouver, a premier conference in its field. The research has attracted attention from various sectors, eager to harness the potential of AI in drug development.
The announcement comes as the pharmaceutical industry increasingly turns to AI to streamline drug development processes. With the potential to cut down on time and costs, AI frameworks like CGFlow could be instrumental in bringing new drugs to market more efficiently.
As the world grapples with complex health challenges, the integration of AI in drug discovery represents a promising avenue for innovation and advancement. The collaboration between academia and industry will be crucial in realizing the full potential of these technologies.
For further information, SFU experts Tony Shen and Martin Ester are available for comment.
