Learning Multi-view Molecular Representations with Structured and Unstructured Knowledge

TL;DR

MV-Mol is a novel multi-view molecular representation learning model that integrates chemical structures, biomedical texts, and knowledge graphs to improve molecular property prediction and multi-modal understanding.

Contribution

The paper introduces MV-Mol, a new model that explicitly incorporates multi-view information and heterogeneous knowledge sources for molecular representations.

Findings

MV-Mol outperforms existing methods in molecular property prediction.

MV-Mol achieves state-of-the-art results in multi-modal molecular comprehension.

The two-stage pre-training enhances the quality of learned representations.

Abstract

Capturing molecular knowledge with representation learning approaches holds significant potential in vast scientific fields such as chemistry and life science. An effective and generalizable molecular representation is expected to capture the consensus and complementary molecular expertise from diverse views and perspectives. However, existing works fall short in learning multi-view molecular representations, due to challenges in explicitly incorporating view information and handling molecular knowledge from heterogeneous sources. To address these issues, we present MV-Mol, a molecular representation learning model that harvests multi-view molecular expertise from chemical structures, unstructured knowledge from biomedical texts, and structured knowledge from knowledge graphs. We utilize text prompts to model view information and design a fusion architecture to extract view-based molecular representations. We develop a two-stage pre-training procedure, exploiting heterogeneous data of varying quality and quantity. Through extensive experiments, we show that MV-Mol provides improved representations that substantially benefit molecular property prediction. Additionally, MV-Mol exhibits state-of-the-art performance in multi-modal comprehension of molecular structures and texts. Code and data are available at https://github.com/PharMolix/OpenBioMed.