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 produce breakthroughs in the natural sciences by applying 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.
Proceedings of the National Academy of Sciences (2019), pp. 201820657
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
Working at Google provides a unique combination of resources: people, compute, and the freedom to attack big problems.
Designing a meaningful experiment and deeply understanding the result is the critical thread across all of the sciences.
Some of our current and previous partners
In Cell Screening:
- Bill & Melinda Gates Foundation
- Lee Rubin, Harvard University
- Steve Finkbeiner, Gladstone Institutes and the University of California, San Francisco
In Cell State:
- Scott Noggle, New York Stem Cell Foundation Research Institute
In Drug Discovery:
- Vijay Pande, Stanford University
- Michl Binderbauer, Tri Alpha Energy
In Material Science:
- John Gregoire, California Institute of Technology and Joint Center for Artificial Photosynthesis
In Quantum Chemistry:
- Anatole von Lilienfeld, Institute of Physical Chemistry and National Center for Computational Design and Discovery of Novel Materials (MARVEL), Department of Chemistry, University of Basel, Switzerland