"Drug discovery traditionally involves testing thousands of chemical compounds to find ones that might treat diseases. Scientists design molecules, run lab experiments, and conduct trials, but most fail, making the process slow and expensive. AI changes this by acting like a smart assistant that learns from data. In simple terms, AI uses machine learning to study patterns in existing data, such as how proteins interact or how drugs have worked before."
"AI also helps in later stages, such as optimizing clinical trials by analyzing patient data to select better participants or predict side effects. Overall, it could lower costs and bring treatments to market faster, benefiting areas like cancer or rare diseases. Eli Lilly and Nvidia aim to solve tough problems in pharmaceuticals through this lab, located in the San Francisco Bay Area."
AI can process massive datasets to identify potential drug candidates and predict effects, reducing years and costs in traditional discovery. Machine learning models learn from protein interactions and historical drug performance to simulate virtual experiments and prioritize promising molecules without physical tests. AI can optimize clinical trials by analyzing patient data to select better participants and anticipate side effects, lowering failure rates and expediting approvals. The Eli Lilly–Nvidia five-year co-innovation lab will invest up to $1 billion in infrastructure and research, build on an existing supercomputer collaboration, and assemble multidisciplinary teams to tackle complex pharmaceutical challenges.
Read at 24/7 Wall St.
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