Economic Uncertainty Looms as Key Inflation Report Faces Data Collection Challenges This week’s crucial inflation report arrives amid unprecedented circumstances…
A groundbreaking study reveals how data bias has inflated performance metrics in drug discovery AI models. The new GEMS system and PDBbind CleanSplit dataset demonstrate superior generalization by eliminating structural redundancies that previously hampered accurate binding affinity predictions.
Researchers have uncovered significant data bias issues that have been inflating the performance metrics of artificial intelligence models used in drug discovery, according to a recent study published in Nature Machine Intelligence. Sources indicate that structural similarities between training and testing datasets have created a “data leakage” problem, allowing models to achieve artificially high performance through memorization rather than genuine understanding of protein-ligand interactions.
Sabey’s Land Acquisition in Butte Signals Major Data Center Development Sabey Data Centers, a prominent US-based data center developer, has…
