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Contrastive Learning for Self-Supervised SMILES Representation Discovery

Ai Drug Repurposing

SMILES-Based Drug Representation Learning

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Ai Drug Repurposing › SMILES-Based Drug Representation Learning

Contrastive Learning for Self-Supervised SMILES Representation Discovery

This research develops contrastive frameworks that learn meaningful SMILES representations without labeled data by comparing molecular augmentations and structural variations. The approach generates robust molecular embeddings that enhance drug repurposing prediction accuracy without extensive annotation.

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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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