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Contrastive Learning on Molecular Graphs for Cross-Indication Drug Transfer

Ai Drug Repurposing

Graph Neural Networks Drug Target Prediction

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Ai Drug Repurposing › Graph Neural Networks Drug Target Prediction

Contrastive Learning on Molecular Graphs for Cross-Indication Drug Transfer

This research applies contrastive learning frameworks to graph neural networks for learning invariant drug representations transferable across disease indications with similar molecular mechanisms. The approach accelerates discovery of drugs effective against disease phenotypes structurally or functionally distinct from original therapeutic targets.

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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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