Molecular Property

Molecular property prediction aims to accurately forecast various characteristics of molecules using computational methods, primarily to accelerate drug discovery and materials science. Current research heavily utilizes machine learning, employing diverse architectures like graph neural networks, transformers, and diffusion models, often incorporating multi-modal data (e.g., combining graph and textual information) and transfer learning strategies to improve prediction accuracy and efficiency, even in data-scarce scenarios. These advancements enable faster and more cost-effective screening of potential drug candidates and materials, significantly impacting the speed and efficiency of scientific discovery.