AI & Data Convergence

Finding and validating quality targets early on in discovery is important because it impacts clinical failure and drug approval rates down the line. With ARCH, our scientists can aggregate multiple pieces of knowledge about diseases from many different sources, visualize patterns and connections among them, and conduct data analyses to figure out which genes or proteins they should pursue.

Clinical trial sites are crucial to the drug development process, providing a controlled, safe environment to test a drug candidate in patients. AbbVie uses AI- and ML-powered advanced analytics tools to tap into decades of data to predict which clinical sites will get up and running quickly and can help deliver on study objectives.

AbbVie is innovatively applying digital health technology (DHT) like biosensors and wearables to collect real-world data in real time, helping reduce the burden on clinical trial participants and give our researchers a better view of the individual’s condition and how the treatment is working. 

Learn more about DHT and the digital science lab.

AbbVie researchers are using machine learning to better understand the similarities and differences in genes, proteins and other potential biomarkers among patients affected by the same disease and using that knowledge to identify potential drug targets for that specific patient population. 

Learn more about precision medicine.

With a drug candidate chosen, scientists next focus on identifying and optimizing compounds that have the potential to be turned into future therapeutics. Today, AbbVie scientists are using generative AI to recognize patterns in the ‘language’ of proteins to predict new protein structures that have the potential to treat disease.

AbbVie’s regulatory team is leveraging AI and automated tools to review clinical trial data and prepare thorough submissions to global regulatory agencies, helping shorten submission timelines while continuing to deliver high quality dossiers.