Positional embeddings and zero-shot learning using BERT for molecular-property prediction.

ABSTRACT: Recently, advancements in cheminformatics such as representation learning for chemical structures, deep learning (DL) for property prediction, data-driven discovery, and optimization of chemical data handling, have led to increased demands for handling chemical simplified molecular input line entry system (SMILES) data, particularly in text analysis tasks. These advancements have driven the need to optimize components like positional encoding and positional embeddings (PEs) in transformer model to better capture the sequential and contextual information embedded in molecular representations. SMILES data represent complex relationships among atoms or elements, rendering them critical for various learning tasks within the field of cheminformatics. This study addresses the critical challenge

SUBMITTER: Mswahili ME 

PROVIDER: S-EPMC11800558 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature