Djork-Arné Clevert

Model Validation Protocols for Machine Learning in Small Molecule Drug Discovery

The paper presents a model validation framework for molecular property prediction models in drug discovery, revealing four failure modes and showing that common evaluation protocols overestimate performance.

Phenotypic reversion and target prioritization for cellular inflammation via representation learning with foundation models

The paper uses single-cell foundation models to identify genetic targets that reverse inflammation in endothelial cells by analyzing Perturb-seq data with and without proinflammatory stimulation.

Large-scale experimental validation of phenotype-guided generative AI for de novo drug discovery

The paper validates a generative AI model using experimental assays to design small molecule drugs targeting specific cancer-related phenotypes, showing 88% morphological impact and 37% transcriptomic pathway effects.

JUMP Cell Painting chemical dataset: Morphological map of 115,796 compounds in human cells

Cell Painting images and profiles for 115,796 compounds in U2OS cells from 12 laboratories; 15.2% of analyzed compounds are phenotypically active, and cross-laboratory replicates cut reference-compound activity by 34%.

Deep Representation Learning Determines Drug Mechanism of Action from Cell Painting Images

The paper introduces MOAProfiler, a deep learning encoder that improves the accuracy of drug mechanism of action classification from Cell Painting images compared to traditional and existing deep learning methods.

PAVOOC: Designing CRISPR sgRNAs using 3D protein structures and functional domain annotation

PAVOOC is a CRISPR sgRNA design tool that uses machine learning to prioritize effective sgRNAs and visualizes their effects on protein structures and functional domains.