01
PerturbRx
Treatment-conditioned representation learning that models intervention-induced biological state transitions and transfers perturbational knowledge toward patient-level drug-response prediction.
Yoshitaka Inoue · UMN / NIH
Learning how biological systems respond to therapeutic intervention.
I develop AI models of treatment-conditioned biological state transitions, with the goal of transferring what we learn from molecular and cellular perturbations to patient-level therapeutic response.
Research
Modeling biological dynamics under intervention
My central question is how a biological system changes after an intervention—and which aspects of those dynamics are transferable across drugs, cellular contexts, modalities, and patients.
Biological state
& context
+
& context
Therapeutic
intervention
intervention
State transition
& dynamics
& dynamics
Transfer across contexts
Predict therapeutic response
Explain mechanism
Better intervention
models
models
Selected work
From therapeutic response to biological dynamics
02
drGT
Structured representation learning over drug–cell–gene relationships to predict therapeutic response while exposing biologically meaningful gene-level mechanisms.
03
DrugAgent
Mechanistic reasoning over heterogeneous and conflicting biomedical evidence, connecting structured knowledge with data-driven therapeutic discovery.
Selected publications
Representative papers
- 2026PerturbRx: Treatment-Conditioned Biological State Transitions for Drug Response Prediction
- 2026drGT: Interpretable Drug Response Prediction with Attention-Guided Gene Attribution on a Drug-Cell-Gene Heterogeneous Graph
- 2026TRPM4 Expression as a Predictive Biomarker and a Mechanistic Driver of Acetalax Activity in Prostate Cancer: Preclinical Efficacy Studies
Recent
PerturbRx preprint released.
ISMB 2026 oral presentations for drGT and DrugAgent.
CCR-FYI 2026 Outstanding Postgraduate Fellow finalist.