Semantic Embeddings are vector representations of data such as text, images, or graphs that capture underlying meaning and relationships in a continuous numerical space. Generated by machine learning models, these embeddings position similar items closer together, enabling tasks such as similarity search, clustering, and retrieval. Semantic embeddings are widely used in natural language processing, recommendation systems, and multimodal learning to support efficient and meaningful data representation.
Designing drug molecules is no longer just about generation, but control. NEC Laboratories America introduces MolDiffdAE, a diffusion-based framework that enables precise, multi-objective tuning of 3D molecular properties. By learning a semantic space, researchers can efficiently guide design, accelerating drug discovery and exploration of chemical space.