# RxRx3-core: Benchmarking drug-target interactions in High-Content Microscopy

arXiv: https://arxiv.org/abs/2503.20158 · HTML: https://mlsanity.com/paper/2503.20158/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 31 references that are in the map.
- First posted on arXiv 26 Mar 2025.

## Builds on (most cited first, 4 of 4)

- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology](https://mlsanity.com/paper/2404.10242/) (2024)
- [ViTally Consistent: Scaling Biological Representation Learning for Cell Microscopy](https://mlsanity.com/paper/2411.02572/) (2024)
- [MOTIVE: A Drug-Target Interaction Graph For Inductive Link Prediction](https://mlsanity.com/paper/2406.08649/) (2024)

## Built on by (most cited first, 11 of 11)

- [CellCLIP -- Learning Perturbation Effects in Cell Painting via Text-Guided Contrastive Learning](https://mlsanity.com/paper/2506.06290/) (2025)
- [Statistical and structural identifiability in representation learning](https://mlsanity.com/paper/2603.11970/) (2026)
- [Efficient Cell Painting Image Representation Learning via Cross-Well Aligned Masked Siamese Network](https://mlsanity.com/paper/2509.19896/) (2025)
- [Compressing Biology: Evaluating the Stable Diffusion VAE for Phenotypic Drug Discovery](https://mlsanity.com/paper/2510.19887/) (2025)
- [JUMP-lite: Compact, reproducible benchmarking of cell representations](https://mlsanity.com/paper/2608.07632/) (2026)
- [Deep Learning for BioImaging: What Are We Really Learning?](https://mlsanity.com/paper/2603.13377/) (2026)
- [Intervention-Aware Multiscale Representation Learning from Imaging Phenomics and Perturbation Transcriptomics](https://mlsanity.com/paper/2604.22832/) (2026)
- [AURA: Active-Response Attribution under Treatment Ambiguity in Bacterial Cytological Profiling](https://mlsanity.com/paper/2606.16477/) (2026)
- [Same Encoder, Different Winner: A Paired-View Framework for Cell Painting Encoder Evaluation](https://mlsanity.com/paper/2609.12761/) (2026)
- [From Retrieval to Reasoning: Agentic Mechanism Prediction from Cell Painting Profiles](https://mlsanity.com/paper/2609.36406/) (2026)
- [Can phenotypic activity be predicted without experimental readouts?](https://mlsanity.com/paper/2610.07997/) (2026)

## Abstract (arXiv)

High Content Screening (HCS) microscopy datasets have transformed the ability to profile cellular responses to genetic and chemical perturbations, enabling cell-based inference of drug-target interactions (DTI). However, the adoption of representation learning methods for HCS data has been hindered by the lack of accessible datasets and robust benchmarks. To address this gap, we present RxRx3-core, a curated and compressed subset of the RxRx3 dataset, and an associated DTI benchmarking task. At just 18GB, RxRx3-core significantly reduces the size barrier associated with large-scale HCS datasets while preserving critical data necessary for benchmarking representation learning models against a zero-shot DTI prediction task. RxRx3-core includes 222,601 microscopy images spanning 736 CRISPR knockouts and 1,674 compounds at 8 concentrations. RxRx3-core is available on HuggingFace and Polaris, along with pre-trained embeddings and benchmarking code, ensuring accessibility for the research community. By providing a compact dataset and robust benchmarks, we aim to accelerate innovation in representation learning methods for HCS data and support the discovery of novel biological insights.