Scientific Materials

Article thumbnail graphic

Masked Autoencoders Are Scalable Learners of Cellular Morphology

arXiv
2023
Article thumbnail graphic

TUPELO Trial: A Phase 2, Multicenter, Randomized, Double-Blind, Placebo-Controlled Trial to Evaluate Efficacy, Safety, Pharmacokinetics, and Pharmacodynamics of REC-4881 in Subjects With Familial Adenomatous Polyposis (FAP): Study Design

11th Annual Clinical Cancer Genetics and Genomics Conference
2023
Article thumbnail graphic

High-Resolution Genome-Wide Mapping of Chromosome-Arm-Scale Truncations Induced by CRISPR-Cas9 Editing

bioRxiv
2023
Article thumbnail graphic

A Phenomics Platform Combining Imaging and Artificial Intelligence for Rapid Validation and Advancement of Novel Oncology Targets

AACR Annual Meeting
2023
Article thumbnail graphic

Biological Cartography: Building and Benchmarking Representations of Life

Learning Meaningful Representations of Life (LMRL) Workshop at NeurIPS
2022
Article thumbnail graphic

Multi-Objective GFlowNets

AI for Accelerated Materials Design (AI4Mat) Workshop at NeurIPS
2022
Article thumbnail graphic

MolE: A Molecular Foundation Model for Drug Discovery

Learning Meaningful Representations of Life (LMRL) Workshop at NeurIPS
2022
Article thumbnail graphic

Obtaining Cellular Morphological Embeddings Across Experimental Batches

CytoData Symposium
2022
Article thumbnail graphic

POPLAR-NF2: A Parallel-Group, Two-Staged, Phase 2/3, Randomized, Multicenter Study to Evaluate the Efficacy and Safety of REC-2282 in Participants With Progressive NF2-Mutated Meningiomas

NF Conference
2022
Article thumbnail graphic

Identification and Optimization of Novel Small Molecule Modulators of Immune Checkpoint Resistance with a Unified Representation Space for Genomic and Chemical Perturbations

AACR Annual Meeting
2022
Article thumbnail graphic

Mapping Biology with a Unified Representation Space for Genomic and Chemical Perturbations to Enable Accelerated Drug Discovery

NeurIPS Learning Meaningful Representations of Life Workshop
2021
Article thumbnail graphic

WILDS: A Benchmark of in-the-Wild Distribution Shifts

International Conference on Machine Learning
2021
Article thumbnail graphic

Functional Immune Mapping with Deep-Learning Enabled Phenomics Applied to Immunomodulatory and COVID-19 Drug Discovery

bioRxiv
2020
Article thumbnail graphic

Identification of Potential Treatments for COVID-19 through Artificial Intelligence-Enabled Phenomic Analysis of Human Cells Infected with SARS-CoV-2

bioRxiv
2020
Article thumbnail graphic

Cell Painting, a High-Content Image-Based Assay for Morphological Profiling Using Multiplexed Fluorescent Dyes

Nature Protocols
2016
Article thumbnail graphic

A New Phenotypic Lexicon for Accelerated Translation

Circulation
2015
Article thumbnail graphic

Strategy for Identifying Repurposed Drugs for the Treatment of Cerebral Cavernous Malformation

Circulation
2014

Open Source Data Sharing

We believe in the benefits of open source and open science, and that by releasing open datasets, we drive value for us and society as a whole. Visit www.rxrx.ai to explore our released datasets.

Our Public Datasets

COVID-19

In 2020, we acted boldly to contribute data to the scientific community in hopes it would be useful in fighting the COVID-19 pandemic. We partnered with a biosafety level 3 facility to infect a variety of human cells with live SARS-CoV-2 virus, and used our platform to investigate the therapeutic potential of a library of approved drugs. We released our findings as an open-source dataset for the scientific community in April of 2020, which can be downloaded here.

Following our initial dataset release, we used our platform to model and screen for therapeutics that can treat the most severe forms of COVID-19 that have progressed to acute respiratory distress syndrome (ARDS). We modeled the cytokine storm associated with late-stage COVID-19, treated it with a library of approved drugs, and in August 2020, released the first morphological dataset representing inflammatory effects and potential treatments in the context of COVID-19 ARDS, along with a preprint of our findings.

RxRx Series

We have also released some of the largest open-sourced biological datasets in the world, the RxRx series, under terms that allow for broad academic and non-commercial use. As part of the series, we also released a preprint that demonstrates the capabilities of Recursion’s platform to model complex immune biology and screen for new therapeutics. To explore our released datasets, please visit our website at www.rxrx.ai. Our contribution to a greater understanding of human biology is just as important as the medicines we advance.