arXiv Computer Vision August 25, 2026 By Sukhrobbek Ilyosbekov (Northeastern University), Shubham Gajjar (Northeastern University), Rongfei Jin (Northeastern University)

MorphoCLIP: Text-Supervised Contrastive Learning for Perturbation Matching in Cell Painting Images

Read the original on arXiv Computer Vision →

The Flow has not summarised this story yet — read it at arXiv Computer Vision.

safety

arXiv AI

Jun 3

CP-Agent: Context-Aware Multimodal Reasoning for Cellular Morphological Profiling under Chemical Perturbations

arXiv:2606. 03435v1 Announce Type: new Abstract: Cell Painting combines multiplexed fluorescent staining, high-content imaging, and quantitative analysis to generate high-dimensional phenotypic readouts to support diverse downstream tasks such as mechanism-of-action (MoA) inference, toxicity prediction, and construction of drug-disease atlases.

By Yuxin Zhang, Yiyao Li, Ping Shu Ho, Simon See, Zhenqin Wu, Kevin Tsia

llms agents multimodal safety

arXiv Machine Learning

Sep 29

Improving Molecular-Morphology Contrastive Pretraining using Deep-Learning-based Morphology Profiles

The paper presents MoCoP v2, an enhanced contrastive pretraining method that aligns small molecule embeddings with deep‑learning‑derived cell morphology profiles. By replacing CellProfiler fingerprints with richer image‑encoded features, the new embeddings better capture how molecules alter cell morphology, leading to improved QSAR, toxicity, ADME, and activity predictions. Performance scales log‑linearly with training data size, indicating further gains with larger datasets.

By Jie Li, Kathryn E. Kirchoff, Dante A. Pertusi, Zhizhuo Zhang

rag fine-tuning benchmarks safety

arXiv AI

Jul 28

scMIR: a vision-language foundation model for single-cell light microscopy image representation

arXiv:2607. 22712v1 Announce Type: cross Abstract: Single-cell light microscopy images have become an important data source for characterizing cell phenotypes, but their complexity and heterogeneity pose challenges to high-throughput automated analysis.

By Yifan Shang (Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, China, College of Computer Science and Electronic Engineering, Hunan University, Changsha, China), Jiahui Tan (College of Computer Science and Electronic Engineering, Hunan University, Changsha, China), Xiangxiang Zeng (College of Computer Science and Electronic Engineering, Hunan University, Changsha, China), Renjie Zhou (Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, China)

fine-tuning efficiency multimodal benchmarks safety

arXiv Computer Vision

Sep 1

JUMP-lite: Compact, reproducible benchmarking of cell representations

arXiv:2608.07632v2 Announce Type: replace-cross Abstract: Image-based profiling captures rich phenotypic signatures for drug discovery and functional genomics. Large public datasets like JUMP Cell Pa...

By Al\'an F. Mu\~noz, Johan Fredin Haslum, Runxi Shen, Anne E. Carpenter, Shantanu Singh

benchmarks

arXiv Computer Vision

Oct 8

Label-free cell counting and viability prediction with brightfield imaging and deep learning

arXiv:2610.10473v1 Announce Type: new Abstract: Cell viability assessment is a core requirement in cell culture systems, with critical applications in biopharmaceutical manufacturing and drug develop...

By Amir Reza Vazifeh, Christian Zeigler, Sornanathan Meyyappan, Richard Jeske, Jason W. Fleischer

agents benchmarks

arXiv Machine Learning

Oct 7

Can phenotypic activity be predicted without experimental readouts?

arXiv:2610.07997v1 Announce Type: new Abstract: Molecular encoders contrastively pretrained on paired molecule-morphology data, such as CLOOME and CellCLIP, have been proposed as cheap surrogates for...

By T\'elio Cropsal, Roc\'io Mercado

safety