Drug Discovery and AI: Commercial Research Worth Talking About
Are you behind in drug discovery because you aren't using AI to its fullest potential?
At DrugBank we work tirelessly to equip leading data scientists with the most in-depth, highest-quality, and up-to-date drug data on the market. As a result, we get to see firsthand how the industry and new technology are transforming the future of medicine and healthcare.
AI has changed the way drug discovery is done. Below we’ve profiled a number of impressive commercial cases that are leveraging AI-powered drug discovery in truly remarkable ways. For an even more in-depth exploration of each case be sure to download our AI in Drug Discovery eBook.
Insilico Medicine
Insilico Medicine is a Hong Kong, China-based company specializing in the development of new AI technologies.
WHAT THEY’RE UP TO
Insilico has made it their mission to accelerate drug discovery and drug development by continuously inventing and deploying new artificial intelligence technologies. Nearly a decade old, they now have several oncology candidates in their pipeline and are pursuing the development of both drugs and biomarkers in areas ranging from fibrosis, infectious diseases, immunology, and the process of aging.
HOW THEY’RE USING AI
One of the most noteworthy advancements Insilico has developed involved applying a generative pipeline to complete hit discovery, optimization, synthesis, and validation on candidates against discoidin domain receptor 1 (DDR1), a kinase target implicated in fibrosis and other diseases.
Insilico has also advanced our understanding of aging. Applying several supervised machine learning approaches, including neural networks, Insilico built a panel of tissue-specific biomarkers of aging.
WHY WE’RE SO IMPRESSED
Insilico’s DDR1 research was able to save significant development time, completing the process in 46 days which was 15-fold faster than traditional approaches.
Insilico was also able to identify the genes most important for age prediction11, achieving Pearson correlation of 0.91for the actual age values of the muscle tissue samples.
Celaris Therapeutics
Celaris Therapeutics is a deep learning company focused on developing therapeutics that work to degrade disease-producing proteins. Based in the US, they also have a presence in Austria.
WHAT THEY’RE UP TO
Celeris Therapeutics focuses on undruggable pathogenic proteins that cause serious conditions such as Alzheimer’s and Parkinson’s disease. Their current pipeline includes programs in neurology and oncology. Celeris is also using graph neural networks to predict the properties of molecules.
HOW THEY’RE USING AI
In their Xanthos Match Maker platform, Celeris encodes molecular structures in a graph along with features such as the number of hydrogens, valence, and aromaticity and then applies deep neural networks where information about molecules and proteins are processed into an increasingly high-level form.
WHY WE’RE SO IMPRESSED
Drugs are currently limited in their ability to treat diseases caused by pathogenic proteins.
By establishing reliable, AI-backed methods to leverage the body’s natural cell-based mechanisms to degrade these proteins, they were able to identify novel potential treatment options.
Cyclica
Cyclica is a drug discovery company headquartered in Toronto, Canada, with teams also located in the US and UK. They work to harness AI and machine learning and utilize a custom-built interactome library to model potential protein interactions.
WHAT THEY’RE UP TO
Cyclica takes an interdisciplinary, collaborative approach to identifying molecules that address protein malfunction. The company leverages polypharmacology, a method of concurrently evaluating interactions, to discover new drugs.
HOW THEY’RE USING AI
Cyclica’s machine learning platform, MatchMaker, combines features derived from protein targets and small molecules to distinguish binding from non-binding protein-ligand pairs. MatchMaker, trained on ~1.5M human bioactivities (including DrugBank), innovates on existing drug-target interaction models by augmenting the protein representations with structural data.
WHY WE’RE SO IMPRESSED
Cyclica’s platform MatchMaker, with its unique approach and generalization capacities, has demonstrated an ability to improve its speed, efficiency, and success rates when predicting potential molecules of interest.
AI is making it possible for researchers to do more, faster, and with greater accuracy. If you’re interested in diving even deeper into how each of these cases are using AI, exploring additional resources, or checking our sources download our latest eBook. Or, if you want to learn more about how our drug data can help kick your AI-powered research into high gear we would love to chat with you.
Looking for more? Check out our first blog in this series and explore how artificial intelligence has changed the drug discovery game.
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