Shot Molecular Property Prediction

Shot molecular property prediction (MPP) aims to accurately predict molecular properties using limited training data, a crucial challenge in drug discovery and materials science. Current research focuses on adapting large language models (LLMs) and graph neural networks (GNNs), often employing meta-learning techniques and incorporating knowledge graphs to leverage relationships between molecules and properties, or using hierarchical embeddings to capture diverse molecular features. These advancements enable more data-efficient and accurate predictions, accelerating the development of new molecules with desired characteristics.