In an all-new podcast, Daniel Faggella talks to Murali Aravamudan, Founder and CEO of AI-driven drug discovery startup Qrativ, a joint venture by the Mayo Clinic and biotech/data science firm nference. Murali and Daniel discuss the surge of medical information and data in the medical industry, the role of artificial intelligence in developing drugs for treatments to various diseases, and the future of AI in drug discovery.
It takes billions of dollars to develop a drug. Machine learning may help make the most of this upfront cost by finding new conditions that might be treated by existing drugs. This “drug repurposing” process is immensely challenging, but AI may be able to use patient data and medical journals to find new application (without more billions spent on developing a drug from scratch).
Drug repurposing, or studying approved drugs used to treat one medical condition and testing its impact to the treatment of other illnesses and rare diseases, is done to speed up the discovery and integration of a new drug in the healthcare industry. It builds on previous research and drug development data to identify which drugs can work in treatment.
Another important information that researchers can use in this field is patient-related data. However, with the enormous amount of genomic information available, it is impossible for researchers to keep up with the pace of data processing without the help of artificial intelligence. Researchers can use neural networks and AI in identifying new therapeutic conditions for medicines.