CheMeleon model adapts to predict molecular properties from scant data

AI Summary

Researchers developed CheMeleon, a model designed to predict molecular properties when only limited data is available. The approach could help reduce trial and error in discovering molecules for applications such as pharmaceuticals, electronics, and energy materials.

Discovering new molecules for specific uses is a major driver of progress in a wide variety of industries, whether it's pharmaceutical companies looking for new drugs or electronics or energy companies seeking new materials with particular properties. But it's also largely a trial-and-error process, because there is not yet any way to perfectly predict how a given chemical compound will behave in terms of the myriad different properties and responses materials can exhibit.