Transformer Based Molecular

Transformer-based models are revolutionizing molecular modeling by learning complex relationships within molecular data, aiming to accelerate drug discovery, materials science, and other fields. Current research focuses on improving the efficiency and diversity of molecule generation, enhancing the representation of molecular geometry and long-range interactions within sequences (e.g., using novel masking strategies and architectures like Hyena), and developing models capable of handling both 2D and 3D molecular data. These advancements enable more accurate predictions of molecular properties and reaction mechanisms, leading to significant improvements in the speed and efficiency of computational chemistry and related applications.