Understanding the sources of performance in deep drug response models reveals insights and improvements.

ABSTRACT:

Motivation

Anti-cancer drug response prediction (DRP) using cancer cell lines (CLs) is crucial in stratified medicine and drug discovery. Recently, new deep learning models for DRP have improved performance over their predecessors. However, different models use different input data types and architectures making it hard to find the source of these improvements. Here we consider published DRP models that report state-of-the-art performance predicting continuous response values. These models take chemical structures of drugs and omics profiles of CLs as input.

Results

By experimenting with these models and comparing with our simple baselines, we show that no performance comes from drug features, instead, performance is due to the transcriptomics CL profiles. Furthermore, we sh

SUBMITTER: Branson N 

PROVIDER: S-EPMC12261491 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature