Molecular Property Prediction

Molecular property prediction (MPP) aims to computationally determine a molecule's characteristics, such as reactivity or toxicity, using machine learning. Current research heavily emphasizes multi-task learning, leveraging transfer learning between related properties and employing diverse model architectures including graph neural networks (GNNs), transformers, and large language models (LLMs), often incorporating multimodal data (e.g., SMILES strings, molecular graphs, and images). Accurate MPP accelerates drug discovery, materials science, and other fields by reducing the need for expensive and time-consuming experimental validation, enabling faster identification of molecules with desired properties.