Spotlight
in
Workshop: Machine Learning for Drug Discovery (MLDD)

Predicting single-cell perturbation responses for unseen drugs

Leon Hetzel · Simon Boehm · Niki Kilbertus · Stephan G√ľnnemann · Mohammad Lotfollahi · Fabian Theis

Keywords: [ perturbation ] [ transfer learning ] [ disentanglement ] [ drug discovery ]

[ Abstract ] [ Project Page ]
[ OpenReview
Fri 29 Apr 7:40 a.m. PDT — 7:45 a.m. PDT
 
presentation: Machine Learning for Drug Discovery (MLDD)
Fri 29 Apr 6 a.m. PDT — 2:30 p.m. PDT

Abstract:

Single-cell transcriptomics enabled the study of cellular heterogeneity in response to perturbations at the resolution of individual cells. However, scaling high-throughput screens (HTSs) to measure cellular responses for many drugs remains a challenge due to technical limitations and, more importantly, the cost of such multiplexed experiments. Thus, transferring information from routinely performed bulk RNA-seq HTS is required to enrich single-cell data meaningfully.We introduce a new encoder-decoder architecture to study the perturbational effects of unseen drugs. We combine the model with a transfer learning scheme and demonstrate how training on existing bulk RNA-seq HTS datasets can improve generalisation performance. Better generalisation reduces the need for extensive and costly screens at single-cell resolution. We envision that our proposed method will facilitate more efficient experiment designs through its ability to generate in-silico hypotheses, ultimately accelerating targeted drug discovery.

Chat is not available.