Jordi Carreras Puigvert

The paper compares single-cell and aggregated morphological profiling to identify programmed cell death mechanisms, finding aggregated data performs better in supervised models.

A human-supervised multi-agent LLM system covered drug repurposing from knowledge graph building to assay analysis in three case studies; retrospective COVID-19 screen AUC-ROC 0.986 without predefined thresholds.

The paper identifies a chemical inducer of ribophagy, SALSa, that reduces toxicity of ALS-related arginine-rich peptides and extends lifespan in a Drosophila model.

This paper introduces LCTP, a workflow for analyzing live-cell time series data to improve mode of action classification by capturing temporal phenotypes across compounds and cell lines.

The paper identifies host-targeting antivirals against SARS-CoV-2 using morphological profiling, finding compounds that reverse infected cell phenotypes and validate host processes hijacked by viruses.

The paper integrates Cell Painting and Thermal Proteome Profiling to infer targets and mechanisms of action for compounds, accurately identifying known MoAs for four out of five tested compounds.

The paper shows that cell counting can predict compound bioactivity as well as Cell Painting or gene expression, but Cell Painting adds unique value in protein-target-specific benchmarks.

This paper introduces a method to apply Cell Painting to 3D spheroids for morphological profiling, detecting phenotypic changes in colorectal cancer cells under compound treatments.

This study shows that combining MDM2 and PARP inhibitors synergistically inhibits p53 wild-type rhabdomyosarcoma growth in vitro and in vivo.

This paper reviews methodological advancements in Cell Painting over a decade, highlighting improvements in protocols, feature extraction, and machine learning applications for biological insights.

The paper identifies clofazimine as a drug that reduces polyQ toxicity by activating PPARγ, improving mitochondrial function in models of polyQ diseases.

The paper develops similarity-based merger models that combine chemical structure and cell morphology predictions to improve assay hit calls across multiple biological assays.

This paper introduces a constraint programming method for designing microplate layouts to reduce bias and batch effects, improving accuracy in dose-response experiments and drug screening.

The paper combines molecular and cell painting image data to predict drug mechanisms of action, showing improved performance with integrated data.

This study compares the predictive performance of models trained on fluorescence and brightfield images for drug mechanism of action prediction, finding comparable results.

The paper combines chemical structure, gene expression, and cell morphology data to improve mitochondrial toxicity prediction in drug discovery.

The paper identifies chemical and genetic regulators of the tetracycline-inducible gene expression system in mammalian cells, showing that FDA-approved drugs and genetic modifiers inhibit the system rather than alleviate TDP-43 toxicity.

This study identifies SPHK inhibitors as a new class of translation suppressors independent of mTOR, linking lipid metabolism to mRNA translation.