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.
About the team
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.
Focus areas
Featured publications
Phys. Rev. Research, vol. 2 (2020), pp. 033402
Journal of Medicinal Chemistry (2020)
Phys. Rev. Research, vol. 2 (2020), pp. 023074
Scientific Reports, vol. 9 (2019), pp. 10752
Proceedings of the National Academy of Sciences (2019), pp. 201814058
SLAS DISCOVERY: Advancing Life Sciences R\&D, vol. 0 (2019), pp. 2472555219857715
Nature, vol. 572 (2019), pp. 27
Proceedings of the National Academy of Sciences (2019), pp. 201820657
initial Submission to BioArxiv (2019)
The International Conference on Machine Learning, Workshop on Climate Change (2019)
BMC Bioinformatics, vol. 19 (2018), pp. 77
Journal of Chemical Theory and Computation (2017)
Journal of Computer-Aided Molecular Design (2016), pp. 1-14
arXiv:1502.02072 [stat.ML] (2015)
Proceedings of IEEE InfoVis 2014, IEEE
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2019) (to appear)
Our work
We launch the Chemome Initiative as we present an effective new method for finding biologically active molecules using a combination of physical and virtual screening.
Understanding how genes work together as a system is core to understanding all living things, which drives interest in this microorganism.
The world’s fastest supercomputers were designed for modeling physical phenomena, yet they still are not fast enough to robustly predict the impacts of climate change, to design controls for airplanes based on airflow or to accurately simulate a fusion reactor.
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 exciting to enable scientific discoveries by applying cutting edge machine learning techniques to hard problems in the physical sciences to help explain them.
Systematically replacing heuristics with machine learning has the potential to transform almost every area of science.
Some of our current and previous partners
Biology:
- Bill & Melinda Gates Foundation
- New York Stem Cell Foundation Research Institute
- Jake Baum, Imperial College London
- Calico
- Steve Finkbeiner, Gladstone Institutes and the University of California, San Francisco
- Lee Rubin, Harvard University
Energy:
- TAE Systems
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
- Vijay Pande, Stanford University
Materials:
- John Gregoire, California Institute of Technology and Joint Center for Artificial Photosynthesis
- Jonathan Fan, Stanford University