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 this https URL

通过多视角分子表示学习，MV-Mol 模型从化学结构、生物医学文本和知识图谱中提取多视图分子专业知识，并在分子性质预测和多模态分子结构与文本理解方面展现出卓越的性能。