MolMVC: Enhancing molecular representations for drug-related tasks through multi-view contrastive learning.

ABSTRACT:

Motivation

Effective molecular representation is critical in drug development. The complex nature of molecules demands comprehensive multi-view representations, considering 1D, 2D, and 3D aspects, to capture diverse perspectives. Obtaining representations that encompass these varied structures is crucial for a holistic understanding of molecules in drug-related contexts.

Results

In this study, we introduce an innovative multi-view contrastive learning framework for molecular representation, denoted as MolMVC. Initially, we use a Transformer encoder to capture 1D sequence information and a Graph Transformer to encode the intricate 2D and 3D structural details of molecules. Our approach incorporates a novel attention-guided augmentation scheme, leveraging prior knowledge to cr

SUBMITTER: Huang Z 

PROVIDER: S-EPMC11373324 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature