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Binding Affinity Prediction with Graph Neural Networks

  • The repository contains the notebooks for the final chapter of my Master's Thesis.

  • The notebooks contain the implementation of a novel fusion-based architecture, which combines a graph neural network (AttentiveFP) and biologically-motivated learned protein embeddings (ProtTransBert) for drug-target interaction prediction. The model architecture is outlined below: proteinafp

The model is benchmarked on Davis and KIBA standard datasets, as well as on an in-house dataset of ~400K de novo generated molecules against 6 targets.