Generative Chemistry
Training machine learning to identify relevant project candidates
Nouvelles et reportages
Research @ Microsoft 2022: A look back at a year of accelerating progress in AI
2022 has seen remarkable progress in foundational technologies that have helped to advance human knowledge and create new possibilities to address some of society’s most challenging problems. Significant advances in AI have also enabled Microsoft to bring new capabilities to…
AI4Science to empower the fifth paradigm of scientific discovery
| Christopher Bishop
Editor’s note, Oct. 20, 2023 – The post was updated to remove information related to the Amsterdam lab, as those details have since changed. Over the coming decade, deep learning looks set to have a transformational impact on the natural…
MoLeR: Creating a path to more efficient drug design
| Krzysztof Maziarz, Marc Brockschmidt, et Marwin Segler
Drug discovery has come a long way from its roots in serendipity. It is now an increasingly rational process, in which one important phase, called lead optimization, is the stepwise search for promising drug candidate compounds in the lab. In…
FS-Mol: Bringing Deep Learning to Early-Stage Drug Discovery
| Marc Brockschmidt et Megan Stanley
The drug development process is an iterative one that consists of discovery, design, and testing. Historically, drugs were derived from plants and discovered through trial-and-error experiments. Fortunately, this drug discovery process now occurs in a lab, with each iteration of…
Minimizing trial and error in the drug discovery process
| Alex Gaunt, Marc Brockschmidt, et Miltos Allamanis
In 1928, Alexander Fleming accidentally let his petri dishes go moldy, a mistake that would lead to the breakthrough discovery of penicillin and save the lives of countless people. From these haphazard beginnings, the pharmaceutical industry has grown into one…