MoleculeNet Benchmark

MoleculeNet is a benchmark dataset used to evaluate machine learning models for predicting various molecular properties, crucial for drug discovery and materials science. Current research focuses on improving prediction accuracy through advanced model architectures, including graph neural networks (GNNs), transformers, and multimodal approaches that integrate different molecular representations (e.g., images and graphs). These efforts leverage techniques like self-supervised and multi-task learning, aiming to capture complex structural information and relationships between molecular properties. Improved prediction accuracy on MoleculeNet translates to more efficient and effective design of new molecules with desired characteristics.