Design and scale experimental datasets for ML Develop workflows that connect noisy experimental signals to actionable model insights 1. Bridging Models with Real-World Experimental Signals Develop met...

Invent novel deep learning techniques for models of (bio)molecular structure, dynamics, reactivity and function. Design, implement, and iterate on model architectures and training algorithms (e.g., di...

Machine-learned force fields are becoming increasingly popular but suffer from their “black-box” nature. Here the authors adapt explainable AI techniques to coarse-grained graph neural network potenti...

A new collaboration between researchers in the Crick’s Visual Biochemistry Lab and Microsoft Research AI for Science will combine cutting-edge laboratory experiments with artificial intelligence, to b...

Following the sequence and structure revolutions, predicting functionally relevant protein structure changes at scale remains an outstanding challenge. We introduce BioEmu, a deep learning system that...

The open-source tool goes beyond AlphaFold by finding proteins’ multiple equilibrium states and free energies

Following the sequence and structure revolutions, predicting functionally relevant protein structure changes at scale remains an outstanding challenge. We introduce BioEmu, a deep learning system that...