Scientific Materials

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

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2024
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Clinical Pharmacology and Tolerability of REC-994, A Redox-Cycling Nitroxide Compound, in Randomized Phase 1 Dose-Finding Studies

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2024
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Masked Autoencoders Are Scalable Learners of Cellular Morphology

2023
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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

2023
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A Phenomics Platform Combining Imaging and Artificial Intelligence for Rapid Validation and Advancement of Novel Oncology Targets

2023
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Biological Cartography: Building and Benchmarking Representations of Life

2022
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Multi-Objective GFlowNets

2022
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MolE: A Molecular Foundation Model for Drug Discovery

2022
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Obtaining Cellular Morphological Embeddings Across Experimental Batches

2022
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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

2022
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Identification and Optimization of Novel Small Molecule Modulators of Immune Checkpoint Resistance with a Unified Representation Space for Genomic and Chemical Perturbations

2022
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Mapping Biology with a Unified Representation Space for Genomic and Chemical Perturbations to Enable Accelerated Drug Discovery

2021
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WILDS: A Benchmark of in-the-Wild Distribution Shifts

2021
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Functional Immune Mapping with Deep-Learning Enabled Phenomics Applied to Immunomodulatory and COVID-19 Drug Discovery

2020
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Identification of Potential Treatments for COVID-19 through Artificial Intelligence-Enabled Phenomic Analysis of Human Cells Infected with SARS-CoV-2

2020
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Cell Painting, a High-Content Image-Based Assay for Morphological Profiling Using Multiplexed Fluorescent Dyes

2016
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A New Phenotypic Lexicon for Accelerated Translation

2015
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Strategy for Identifying Repurposed Drugs for the Treatment of Cerebral Cavernous Malformation

2014
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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.

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.

Phenom-Beta

We have released the first in a potential series of foundation models for external use (both non-commercial and commercial) hosted on NVIDIA’s BioNeMo platform. We call this model Phenom-Beta. It flexibly processes microscopy images into general-purpose embeddings. In other words, Phenom-Beta can take a series of images and create a meaningful representation of the input image. This enables robust comparison of images, and other data science techniques to decode any biology or chemistry within such images. This allows scientists to systematically relate genetic and chemical perturbations to one another in a high-dimensional space, helping determine critical mechanistic pathways and identify potential targets and drugs. Currently, the model is available through the API and will be available through BioNeMo Beta. Learn more at at www.rxrx.ai.