DTLCDR
Target-based multimodal framework for preclinical cancer drug response prediction and transfer to clinical response, with explicit unseen-drug generalization.
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- Computational Biology
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- 2026-10-10
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EXPRESSO doi.org
Supervised treatment-response framework using pretreatment tumor transcriptomics, drug targets, and context-specific biomarkers across multiple cancer types and therapies.
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PerturbRx arxiv.org
Treatment-conditioned representation learning framework that transfers drug-induced latent transitions learned from single-cell perturbation data to patient-level cancer treatment-response prediction.
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TGSA github.com
Tumor gene set and attention-based model leveraging biological pathway knowledge for drug response prediction.
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THERAPI github.com
Cell-line-to-patient transfer framework that aligns tumor transcriptomes with cancer cell lines and integrates perturbation and gene-level representations for patient drug response prediction.
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CellOT github.com
Neural optimal transport framework for predicting single-cell responses to drug and genetic perturbations.
Computational Biology › Machine Learning Tasks and Models > Drug Discovery > Drug Response Prediction: “Target-based multimodal framework for preclinical cancer drug response prediction and transfer to clinical response, with explicit unseen-drug generalization.”