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AI Cancer Drug Repurposing Pipeline

Ai Drug Repurposing

AI Cancer Drug Repurposing Pipeline

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AI Cancer Drug Repurposing Pipeline

Intern will build end-to-end AI pipelines integrating genomics data and drug response prediction to identify cancer drug repurposing opportunities.

The focused areas below are internship topics in varied working formats. Pick one, then choose your internship type, mode… Read more

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📚 Academic: Thesis & PPT assistance included🧪 Tech: Master the protocols hands-on📝 Research > 3 months: Publication co-authorship in a Scopus-indexed journal

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Showing 1–10 of 10

Machine Learning Protein Structure Prediction for Oncology

Research investigates how deep learning models predict cancer protein targets and their binding mechanisms using structural biology data. This generates novel insights into how existing drug molecules can interact with previously unexplored cancer pathways.

Academic (A)Tech (T)Research (R)

14 more durationsView Titles →

Graph Neural Networks for Drug-Target Interaction Mapping

Study examines graph-based neural architectures to model complex relationships between pharmaceutical compounds and cancer-related protein networks. The research produces predictive frameworks identifying hidden therapeutic connections between approved drugs and new oncology targets.

Academic (A)Tech (T)Research (R)

14 more durationsView Titles →

Transfer Learning Across Cancer Genomic Datasets

Research applies pre-trained AI models to identify cancer mutations that respond to existing drug therapies across heterogeneous tumor types. This generates cross-cancer insights enabling drug repurposing strategies for rare and understudied malignancies.

Academic (A)Tech (T)Research (R)

14 more durationsView Titles →

Natural Language Processing of Oncology Literature Mining

Study uses advanced NLP techniques to extract hidden associations between drug mechanisms, cancer biology, and clinical outcomes from published research. The research produces comprehensive knowledge graphs revealing overlooked repurposing opportunities from scientific literature.

Academic (A)Tech (T)Research (R)

14 more durationsView Titles →

Federated Learning for Multicenter Cancer Drug Data

Research develops decentralized AI models training across multiple hospital networks'' cancer patient datasets without centralizing sensitive information. This generates privacy-preserving insights into how drugs perform across diverse patient populations and tumor subtypes.

Academic (A)Tech (T)Research (R)

14 more durationsView Titles →

Reinforcement Learning for Rational Drug Combination Design

Study employs reinforcement learning agents to discover optimal combinations of repurposed drugs that overcome cancer resistance mechanisms. The research produces computational strategies identifying synergistic drug pairs reducing tumor heterogeneity.

Academic (A)Tech (T)Research (R)

14 more durationsView Titles →

Metabolic Network Analysis via Systems Pharmacology

Research investigates how AI-driven metabolic modeling predicts cancer cell dependencies exploitable by existing pharmaceutical agents. This generates mechanistic understanding of how repurposed drugs disrupt oncogenic metabolic pathways.

Academic (A)Tech (T)Research (R)

14 more durationsView Titles →

Explainable AI for Cancer Drug Mechanism Elucidation

Study develops interpretable machine learning models that reveal why specific drugs show unexpected efficacy against particular cancer types. The research produces trustworthy predictions and mechanistic insights accelerating clinical validation of repurposed therapies.

Academic (A)Tech (T)Research (R)

14 more durationsView Titles →

Temporal Deep Learning for Treatment Response Prediction

Research applies sequential neural networks to patient biomarker trajectories predicting which repurposed drugs optimize individual cancer outcomes. This generates personalized medicine insights matching drugs to patient-specific tumor evolution patterns.

Academic (A)Tech (T)Research (R)

14 more durationsView Titles →

Multimodal Embedding Spaces for Drug-Cancer Phenotype Alignment

Study creates unified vector representations integrating drug chemistry, cancer genomics, and clinical phenotypes through transformer architectures. The research produces semantic spaces where similar drugs and cancer types cluster, revealing novel repurposing candidates.

Academic (A)Tech (T)Research (R)

14 more durationsView Titles →