3d rendered illustration of the anatomy of a cancer cell: © Sebastian Kaulitzki – AdobeStock_305639707 3d rendered illustration of the anatomy of a cancer cell: © Sebastian Kaulitzki – AdobeStock_305639707
The pharmacological universe stands at a critical junction right now. It’s where the world of drug development intersects with artificial intelligence, and we’re just starting to see the first fruits of research into this area. Going beyond wet lab trial and error, artificial intelligence engines designed for biologic precision are accelerating drug candidate identification, particularly in oncology.
Recognition of artificial intelligence in the area of pharmaceuticals received a significant bump this year when the Nobel Prize in Chemistry was awarded to scientists who developed an AI model capable of predicting the complex structures of proteins from their amino acid sequences. 1 In fact, that model has now been used to successfully predict the structure ofalmost all 200 million known proteins. 2 Biologic-specific AI
While we’re still in the early days of applying AI to drug design, the technology is already starting to transform the discovery of biologics. According to researchers based at the Boston Consulting Group (BCG), discovering new biologics is more challenging than designing other types of drugs because of the molecule size. Small-molecule drugs typically have sizes ranging from 200 to 700 Da, so AI can be used directly to design the optimized molecular structure. On the other hand, biologics typically have a size between 5,000 and 200,000 Da, which means current limitations in computing power and technology prevent direct, atom-by-atom design of biologics using AI. 3
Despite those challenges, at least 50 to 60 AI-enabled biologics are already in various stages of discovery and development, a number expected to grow rapidly on the back of further advancements in AI technology, computing power, and data availability. 3 Oncology is an area of particular interest in AI-enabled biologics discovery because current treatments destroy healthy cells alongside malignant ones. BCG identified at least eight AI-derived biologics in clinical trials for oncology.
As a result of the limitations in computing power, AI is used differently in biologics in five specific areas (as […]
Artificial Intelligence Makes Possible a Multiomic Approach in Oncology Drug Discovery
