AI Aims to 'Close the Data Loop' in Drug Discovery
A new approach in AI-driven drug discovery seeks to 'close the data loop,' addressing inefficiencies in the traditional pharmaceutical development process. Integrating AI with experimental feedback is highlighted as a way to potentially accelerate timelines and reduce costs, which have historically doubled every nine years according to Eroom’s Law. This shift is seen as a response to increasing market pressures for faster and more cost-effective drug development.
Why it matters: Improving the efficiency of drug discovery with AI could significantly impact healthcare innovation and patient access to new treatments.
Full story at: MIT Technology Review / AI ↗