We are taking advances in machine learning and artificial intelligence and applying them to accelerate progress in natural science: biomedical research, chemistry, and material science.
Google Accelerated Science
Our mission is to increase the rate of scientific discovery with Google technologies, including machine learning, iterative prediction/experimentation in large combinatorial spaces, and large scale analysis and computation. We believe these will enable more effective high throughput research in many domains.
Using Google's unique expertise, technology and scale, we collaborate with world-class institutions on challenges with large scientific and humanitarian benefit, working closely with leading scientists who have deep domain expertise and proven experimental infrastructure.
BMC Bioinformatics, vol. 19 (2018), pp. 77
Journal of Chemical Theory and Computation (2017)
Journal of Computer-Aided Molecular Design (2016), pp. 1-14
A popular artificial-intelligence method provides a powerful tool for surveying and classifying biological data. But for the uninitiated, the technology poses significant difficulties.
Tri Alpha Energy has a unique scheme for plasma confinement called a field-reversed configuration that’s predicted to get more stable as the energy goes up, in contrast to other methods where plasmas get harder to control as you heat them.
Using our large-scale neural network training system, we trained at a scale 18x larger than previous work with a total of 37.8M data points across more than 200 distinct biological processes.
Our MPNNs set a new state of the art for predicting all 13 chemical properties in QM9.
Some of our people
It's an incredible opportunity to be able to connect the exciting advances in machine learning and biology. Each offer new ways of learning more than we've ever been able to before.
It's amazing to get to get up every day and work with world class scientists, exploring their complex, structured and nuanced data sets.