Nature Machine Intelligence Article
Modelling drug-induced cellular perturbation responses with a biologically informed dual-branch transformer
This paper introduces XPert, a transformer-based model designed to predict drug-induced transcriptional perturbations. XPert utilizes a dual-branch architecture to capture both pre- and post-perturbation cellular states, leveraging context-aware gene network modelling. It significantly outperforms existing VAE-based approaches in generalization tasks (e.g., 36.7% higher Pearson's correlation coefficient in cold-cell scenarios) and precisely resolves pharmacodynamic trajectories. XPert also supports knowledge transfer from preclinical to clinical contexts, improving patient-specific response predictions by up to 15.04%. Furthermore, it offers mechanistic interpretability, identifying clinically validated resistance biomarkers, thereby establishing a transformative tool for drug discovery and personalized therapeutics.
Key Impact & Performance Highlights
XPert sets new benchmarks in predictive accuracy and clinical relevance, driving significant advancements in drug discovery.
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Higher PCC in cold-cell generalization
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Lower MSE in cold-cell generalization
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Improvement in patient-specific predictions
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Model Architecture
XPert employs a dual-branch transformer architecture that separately encodes pre- and post-perturbation cellular states, effectively disentangling intrinsic transcriptional patterns from regulatory shifts. It integrates context-aware gene network modeling and condition tokens for dose-time dynamics. This approach enables superior generalization and mechanistic interpretability.
Benchmark Performance
XPert consistently outperformed state-of-the-art models in single-dose-single-time and multi-dose-multi-time prediction tasks. It achieved significantly higher Pearson's correlation coefficients and lower mean square errors, especially in challenging cold-drug and cold-cell generalization scenarios, addressing limitations of VAE-based models like over-denoising.
Clinical Transferability
To address data scarcity, XPert uses knowledge transfer from large-scale preclinical screens to clinical contexts. This pretrain-fine-tune framework improved patient-specific response predictions by up to 15.04% and identified clinically validated resistance biomarkers, demonstrating its potential for personalized therapeutic development.
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Higher Pearson's Correlation Coefficient in Cold-Cell Generalization compared to best baseline (TranSiGen).
XPert's Biologically Informed Workflow
Unperturbed Cell Gene Expression
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Multiscale Drug Features (Chemical, Biological, Dose, Time)
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Dual-Branch Transformer Encoding
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Context-Aware Gene Network Modelling
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Prediction of Perturbation Effects (Xpert & Xdeg)
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Mechanistic Interpretability & Clinical Insights
| Feature | XPert | VAE-based Models (TranSiGen, PRnet) | Attention-based Models (DeepCE, CIGER) |
|---|---|---|---|
| Architecture |
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|
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| Generalization (Cold-Cell) | Superior (36.7% higher PCC, 78.2% lower MSE) | Suboptimal, negative R² values in blind tests | Suboptimal, sharp performance drop |
| Dose-Time Dynamics | Precisely resolves pharmacodynamic trajectories | Simplistic encoding, inadequate for nonlinear relationships | Limited dose-time modelling (one-hot encoding) |
| Interpretability | Mechanistic insights, identifies resistance biomarkers | Less direct interpretability | Limited mechanistic insights |
Preclinical to Clinical Translation Success
XPert's ability to transfer knowledge from large-scale preclinical data (L1000) to smaller, high-fidelity clinical datasets (CDS_DB) demonstrates a significant leap in drug discovery. By pretraining on L1000 and fine-tuning on CDS_DB, XPert achieved up to 15.04% improvement in patient-specific response predictions for breast cancer. This framework successfully navigated the domain shift between preclinical and clinical data, focusing on conserved interaction mechanisms. Moreover, XPert's attention-based analysis uniquely identified key resistance biomarkers, such as TIAM1, RPCP, HK1, and CDKN1B, which were invisible to expression-level analysis, providing deeper insights into drug resistance mechanisms.
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