MulAFNet: Integrating Multiple Molecular Representations for Enhanced Property Prediction.

ABSTRACT: In computer-aided drug design, molecular representation plays a crucial role. Most existing multimodal approaches primarily perform simple concatenation of various feature representations, without adequately emphasizing effective integration among these features. To address this issue, this study proposes a network framework that integrates multimodal representations using a multihead attention flow (MulAFNet). MulAFNet utilizes SMILES string representation and two levels of molecular graph representations: atom-level and functional group-level graph structure. Pretraining tasks are established for each of these three representations, which are then fused in downstream tasks to predict molecular properties. The experiments were conducted on six classification data sets and three regression

SUBMITTER: Ci L 

PROVIDER: S-EPMC11966294 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature