Conformer Generation

Conformer generation focuses on creating diverse and accurate three-dimensional representations of molecules and other complex structures, crucial for applications like drug discovery and speech recognition. Current research emphasizes the use of diffusion models and transformer-based architectures, such as conformers and mixture-of-experts (MoE) models, to generate these representations efficiently and accurately, often incorporating physical constraints or leveraging large datasets. These advancements improve the prediction of molecular properties, enhance the accuracy of speech recognition systems, and facilitate the development of new materials and therapeutics by providing more realistic and reliable structural information.