Sequential Attachment Based Fragment Embedding

Sequential attachment-based fragment embedding focuses on representing complex structures, like molecules or images, as sequences of smaller, interconnected units (fragments) to facilitate efficient processing and generation. Current research employs various neural network architectures, including transformers and graph neural networks, to learn representations of these fragments and utilize them for tasks such as image reconstruction, molecular design, and property prediction. This approach offers advantages in handling large, complex datasets and enables the development of more efficient and robust models for diverse applications, particularly in areas where traditional methods struggle with scalability or require extensive labeled data.