CGSchNet, a fast machine-learned model, simulates proteins with high accuracy, enabling drug discovery and protein ...

Some of the most encouraging results for reaction-enhancing catalysts come from one material in particular: tin (Sn). While ...

Drug discovery has long been criticized for its slow, costly, and failure-prone nature. Traditional approaches, particularly ...

Learn how AI and machine learning enhance antibody discovery – from structure prediction to binding optimization – with ...

The term dementia is used to describe various debilitating neurological disorders characterized by a progressive loss of memory and a decline in mental abilities. Estimates suggest that over 55 ...

Researchers have used machine learning to dramatically speed up the processing time when simulating galaxy evolution coupled with supernova explosion. This approach could help us understand the ...

Machine learning accelerates catalyst discovery by combining theory, AI, and experiments to identify efficient materials for ...

Machine learning models—especially large-scale ones like GPT, BERT, or DALL·E—are trained using enormous volumes of data.

Model for predicting molecular crystal properties is readily adaptable to specific tasks, even with limited data ...

In today's AI-driven world, AI tools for data analysis have supercharged the ability to extract meaningful insights from vast ...

Springbok Analytics, a leader in AI-driven muscle analytics, today announced the publication in Scientific Reports of a new disease progression model for facioscapulohumeral muscular dystrophy (FSHD).

Seaweed can combat the emissions as a carbon sink – something that absorbs more carbon than it releases – and, when mixed ...