Recursion Pharmaceuticals, Inc.

Maryam Fallah

Mohammadsadegh Saberian

Berton Allen Earnshaw

Peter Foster McLean

Safiye Celik

Maureen Katherine Makes

Ayla Yasmin Khan

Vasudev Sharma

Maciej Sypetkowski

Jessica Wai Yin Leung

Kristen Rose Morse

Jaichitra Balakrishnan

Kian Runnels Kenyon-Dean

Oren Zeev Kraus

Chi Cheng

Dominique Beaini

Benjamin John Mabey

Conor Austin Forsman Tillinghast

Marta Marie Fay

Jordan Michael Sorokin

Arin Minasian

Kelly Anne Zalocusky

The present disclosure relates to systems, non-transitory computer-readable media, and methods that train and utilize machine learning models to generate perturbation embeddings from phenomic images of cells, including neuronal cell images. Indeed, in one or more implementations, the disclosed systems generate a perturbation embedding using an adapter model or a mixture of experts model. In some implementations, the disclosed systems utilize a mixture of experts model that combines phenomic embeddings from different embedding models to generate a mixture of experts phenomap that contains information from multiple embedding models.

Training and utilizing machine learning models to generate perturbation embeddings from phenomic images of cells, including neuronal cell images

The present disclosure relates to systems, non-transitory computer-readable media, and methods for training and utilizing generative machine learning models to generate embeddings from phenomic images (or other microscopy representations). For example, the disclosed systems can train a generative machine learning model (e.g., a masked autoencoder generative model) to generate predicted (or reconstructed) phenomic images from masked version of ground truth training phenomic images. In some cases, the disclosed systems utilize a momentum-tracking optimizer while reducing a loss of the generative machine learning model to enable efficient training on large scale training image batches. Furthermore, the disclosed systems can utilize Fourier transformation losses with multi-stage weighting to improve the accuracy of the generative machine learning model on the phenomic images during training. Indeed, the disclosed systems can utilize the trained generative machine learning model to generate phenomic embeddings from input phenomic images (for various phenomic comparisons).

Utilizing masked autoencoder generative models to extract microscopy representation autoencoder embeddings

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Toronto, Canada

Maryam Fallah