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.
Here's a small sample of our partners.
In Cell Screening:
In Cell State:
In Drug Discovery:
In Materials Science:
In Quantum Chemistry:
Our projects span many fields of science, including biology, chemistry, materials science, and physics. We actively investigate new scientific problems in areas we haven't worked in yet, and maintain a flexible scope on a wide range of potential projects.
We use machine learning to scalably interpret biological images. Our approaches offer many benefits including non-invasively imputing deep cell measures on transmission microscopy images (without stains), and discovering new phenotypes and common mechanism of actions across large cell screens.
We use several machine learning approaches to represent molecules and predict their interactions. We’ve tackled a range of problems using in silico screening of molecules for affinity to specific targets, a useful step in discovering new drugs.
While there exist many computational approaches to accurately simulating molecular or material properties, often these simulations are too expensive to discover molecules or do materials designs at scale. We are using machine learning to predict the outcome of expensive simulations, allowing us to short circuit computationally difficult problems.
We use design of experiment techniques and machine learning to explore large combinatorial spaces with feedback from physical experiments. These problems range from designing a robotic protocol for differentiating stem cells, to determining the right mix of atoms to create a new functional material, to optimizing settings for a plasma fusion machine.
Scientific Reports, vol. 7 (2017), pp. 6425
Journal of Chemical Theory and Computation (2017)
Varun Gulshan, Lily Peng, Marc Coram, Martin C Stumpe, Derek Wu, Arunachalam Narayanaswamy, Subhashini Venugopalan, Kasumi Widner, Tom Madams, Jorge Cuadros, Ramasamy Kim, Rajiv Raman, Philip Q Nelson, Jessica Mega, Dale Webster
Journal of Computer-Aided Molecular Design (2016), pp. 1-14
arXiv:1502.02072 [stat.ML] (2015)