Using AI-Assisted Multi-Modal Data Analysis for Drug Development with Vin Singh Bullfrog AI

Vin Singh, Founder and CEO of Bullfrog AI, discusses how AI is being used to improve the drug development process through a precision medicine approach that looks at a combination of genetic and non-genetic attributes to predict patient response to drugs.  Bullfrog AI aims to reduce the failure rate in late-stage trials by enrolling the right patients in clinical trials. Through their partnership with the Johns Hopkins Lieber Institute for Brain Development, they use AI to analyze a unique dataset of post-mortem brains to discover new drug targets and improve clinical trial design. Vin explains, "Bullfrog is a technology-enabled drug development company. And what we're doing is using a proprietary AI to improve the drug development process, reduce development times and costs for development, and increase access, which benefits the world."   "The pharmaceutical industry is plagued with inefficiencies. It takes 10 to 15 years and one to two billion dollars to develop drugs, and the failure rates in that drug development process are incredibly high. Our goal here is to fix that formula of failure, which will ultimately benefit patients and get them access to drugs that work for them."   "Typically, when you think of AI and machine learning, you think about training the AI to identify or predict something. Typically, the thinking is you need lots and lots of data and lots of patients. With our technology, we can do a shallow and wide analysis. What that means is shallow means not thousands or millions of patients. We can do it with hundreds of patients. Wide means it could be a lot of different types of data, including genomic data. So there might be millions of areas."  #BullfrogAI #AI #DrugDevelopment #ClinicalTrials #LieberInstitute #JohnsHopkinsUniversity  Bullfrogai.com Download the transcript here