Probing carbon capture, atom-by-atom with machine-learning model

A team of scientists at Lawrence Livermore National Laboratory (LLNL) has developed a machine-learning model to gain an atomic-level understanding of CO2 capture in amine-based sorbents. This innovative approach promises ...

Graph learning modules enhance drug-target interaction predictions

The identification of drug-target Interactions (DTIs) represents a pivotal link in the process of drug development and design. It plays a crucial role in narrowing the screening range of candidate drug molecules, thereby ...

Researchers look to rice for 'clean label' ingredients

Naturally occurring polyphenols and proteins from pigmented waxy rice may help starch ingredients improve texture without any chemical modification—a change some consumers may welcome, said Ya-Jane Wang, professor of carbohydrate ...

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