Jessica Lin and Zhenqi (Pete) Shi from Genentech describe a novel machine learning approach to predicting retention times for ...

Machine learning (ML) is increasingly being utilized to optimize the research paradigm and shorten the time from discovery to application of novel functional materials, pharmaceuticals, and fine ...

There is more than one way to describe a water molecule, especially when communicating with a machine learning (ML) model, says chemist Robert DiStasio. You can feed the algorithm the molecule's ...

SOUTH SAN FRANCISCO, Calif.--(BUSINESS WIRE)--insitro, a pioneer in machine learning for drug discovery and development, today announced a new collaboration with Eli Lilly and Company (Lilly) to ...

For decades, scientists have relied on structure to understand protein function. Tools like AlphaFold have revolutionized how researchers predict and design folded proteins, allowing for new ...

While experimental screening of polymer libraries is time-consuming and costly, purely computational approaches have so far fallen short due to limited data availability and high computational demands ...

Discover how Optibrium is transforming early-stage drug discovery through AI-powered software, generative chemistry, and 3D modelling. In this interview, Matt Segall, CEO at Optibrium, shares insights ...

Until now, designing complex metamaterials with specific mechanical properties required large and costly experimental and simulation datasets. The method enables ...

More aggressive feature scaling and increasingly complex transistor structures are driving a steady increase in process complexity, increasing the risk that a specified pattern may not be ...

Imagine being able to program materials to control heat like you can control a light with a dimmer switch. By simply squeezing or stretching the materials, you can make them hotter or colder.