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Graph Autoencoders for Novel Drug-Target Space Exploration

Ai Drug Repurposing

Graph Neural Networks Drug Target Prediction

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Ai Drug Repurposing › Graph Neural Networks Drug Target Prediction

Graph Autoencoders for Novel Drug-Target Space Exploration

This research employs graph autoencoder models to learn compressed latent representations of drug-target networks and generate synthetic compounds with predicted novel binding profiles. The approach discovers underexplored regions of chemical space where existing drugs may possess unexpected off-target activities with therapeutic value.

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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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