Predicting Single-Cell Perturbation Responses for Unseen Drugs

MCML Authors

Leon Hetzel

→ Group Fabian Theis
 Mathematical Modelling of Biological Systems

Niki Kilbertus

Prof. Dr.

Principal Investigator

Ethics in Systems Design and Machine Learning

Stephan Günnemann

Prof. Dr.

Principal Investigator

Data Analytics & Machine Learning

Fabian Theis

Prof. Dr.

Principal Investigator

Mathematical Modelling of Biological Systems

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.

inproceedings

---

MLDD @ICML 2022

Workshop on Machine Learning for Drug Discovery at the 39th International Conference on Machine Learning. Baltimore, MD, USA, Jul 17-23, 2022.

---

Authors

L. Hetzel • S. Boehm • N. Kilbertus • S. Günnemann • M. Lotfollahi • F. J. Theis

---

Links

URL

---

Research Areas

 A3 | Computational Models

 C2 | Biology