Learning Multi-view Molecular Representations with Structured and Unstructured Knowledge
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 multimodal comprehension of molecular structures and texts. Code and
data are available at https://github.com/PharMolix/OpenBioMed.
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