EMERJ helps to deliver the ROI of A.I.

Head of Research & CEO of EMERJ Daniel Faggella joins Todd Eury, CEO & Founder of the Pharmacy Podcast Network to discuss Artificial Intelligence application in drug development.  

The facilitation of research and development (R&D) is perhaps the most common use case for AI applications in the pharmaceutical industry. There are numerous solutions for extracting and organizing research data from clinical trial notes and other medical documents. Additionally, there is software that can purportedly analyze data from images of drug compounds at the molecular level.

AI could also help pharmaceutical companies manufacture newly discovered drugs more efficiently. This is accomplished by making predictions about how the drug may react when manufacturers try to turn it into a pill, liquid medicine, or topical salve, for example. Data scientists can find out if a drug is likely to break down or become less useful when processed into these types of products before the pharmaceutical company actually takes that risk.

In this overview of the possibilities of  AI for pharmaceutical R&D, we cover:

• Natural Language Processing for Drug Discovery: Searching through databases of unstructured data such as digitized past clinical trial reports.
• Predictive Analytics for Drug Discovery: Predicting how certain molecules will react with each other to figure out how effective a drug might be at treating a disease.
• AI for Salt and Polymorph Testing: Using AI to determine the solubility of a drug compound.

We begin our overview of AI for pharmaceutical R&D with the possibilities for natural language processing (NLP) software and how it facilitates the use of unstructured data.

This episode is sponsored by the University of California Irvine, UCI, Master of Science in Pharmacology, learn more: https://sites.uci.edu/mspharmacology/