Deep learning models for chemical perturbation prediction do not yet utilise drug molecular features
Abstract
Abstract Recent deep learning models for L1000 chemical perturbation prediction incorporate dedicated drug molecular encoders. We retrained seven such models from scratch with zeroed or shuffled drug inputs, and compared them with a multilayer perceptron that uses only cell-line basal expression. Under drug-blind evaluation, ablation caused negligible performance changes and the drug-free baseline matched all models. Current architectures do not yet utilise drug molecular features for generalisation to unseen compounds.
Fields of Study & Subjects
Computational Drug Discovery Methods
Machine Learning in Bioinformatics
Cell Image Analysis Techniques
Publication details
- Journal / source
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Publication date
- 15 May 2026
- Document type
- Preprint