Molecular Property Prediction using Graph Neural Networks

Cheminformatics › AI Chemical Graph Neural Network Research

Molecular Property Prediction using Graph Neural Networks

Interns will develop and train GNN models to predict chemical properties (solubility, toxicity, binding affinity) from molecular graphs. They will work with datasets like ZINC and ChEMBL, implementing architectures such as Graph Convolutional Networks and Message Passing Neural Networks to optimize prediction accuracy.

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