Molecular Modeling

Molecular modeling uses computational methods to study the structure, properties, and behavior of molecules, aiming to predict and understand their interactions. Current research heavily emphasizes machine learning, particularly graph neural networks and transformers, to learn molecular representations from diverse data sources (e.g., 2D/3D structures, experimental data, text descriptions), often incorporating techniques like contrastive learning and knowledge distillation for improved accuracy and efficiency. These advancements are accelerating progress in diverse fields, including drug discovery, materials science, and catalyst design, by enabling faster and more accurate predictions than traditional methods.