Small Molecule

Small molecule research focuses on understanding and manipulating the properties of small organic molecules, primarily for applications in drug discovery and materials science. Current research heavily utilizes machine learning, employing diverse architectures like graph neural networks, transformers, and generative models (e.g., variational autoencoders, GFlowNets) to predict molecular properties, design novel molecules with desired characteristics, and accelerate virtual screening processes. This field is crucial for advancing drug development, enabling the efficient design of therapeutics with improved efficacy and safety profiles, and also holds significant potential for materials science applications.