In Montreal, a team of developers at Insilico Medicine are utilizing the power of artificial intelligence (AI) to accelerate the search for life-saving drugs. Led by Dr. Petrina Kamya, the team aims to harness AI’s potential in the pharmaceutical industry, where the process of developing and testing drugs can be resource-intensive and time-consuming.
Insilico Medicine, a biotech company with headquarters in Hong Kong, chose Montreal as their research hub due to the city’s deep roots in medical research and status as a launch pad for the AI boom. Dr. Kamya, the global head of AI platforms, recognizes the potential for growth and integration within Montreal’s ecosystem.
Unlike traditional drug discovery methods, Insilico Medicine’s algorithms employ a “multi-modal” approach, which includes analyzing results from published literature and grant applications to identify promising drug candidates. This approach allows them to navigate the complex landscape of potential therapeutics and expedite the discovery process.
Although AI-guided drug discovery offers the possibility of faster clinical development, Dr. Kamya emphasizes the importance of maintaining realistic expectations. While AI algorithms can help identify potential drugs more efficiently, it is crucial to prove their effectiveness in clinical trials.
The prominence of AI as a biomedical tool was highlighted in 2020 when AlphaFold 2, an algorithm developed by Google DeepMind, dominated a protein structure prediction competition. This breakthrough has paved the way for advancements in understanding the shapes and functions of proteins, pivotal in developing targeted treatments for diseases.
However, a significant obstacle for AI-guided drug discovery is the lack of public data available to train algorithms. Unlike language algorithms that can learn from internet text or protein shape prediction algorithms that use a public database, drug discovery algorithms lack a comprehensive repository of relevant information.
To tackle this challenge, the Structural Genomics Consortium and Princess Margaret Cancer Centre have launched the CACHE challenge. Modeled after the protein structure prediction competition, CASP, the CACHE challenge aims to encourage and identify successful approaches to finding new drugs using computational tools. The competition provides competitors with protein targets to discover small molecules or “hits” for potential therapies.
Montreal’s vibrant AI and medical research community, along with initiatives like the CACHE challenge, position the city as a leading hub for AI-powered drug discovery. As AI continues to revolutionize the healthcare industry, the potential for significant advancements in drug development and patient care is within reach.
The source of the article is from the blog myshopsguide.com