Molecular Sharing and Molecular-Specific Representations for Multimodal Molecular Property Prediction

Molecular property prediction plays a crucial role in drug discovery and development. However, traditional experimental measurements and Quantitative Structure-Activity Relationship (QSAR) models are often expensive, time-consuming, and data acquisition is challenging. To overcome these limitations and challenges, this study innovatively proposes a fusion molecular property prediction method called molecular property prediction model (MSSP) to address the non-uniqueness of Simplified Molecular Input Line Entry System (SMILES) string representation and the difficulty of capturing global information in molecular graphs. This method extracts multiple fingerprint features and utilizes graph neural network encoding to map different modalities of molecules into molecular sharing and molecular-specific representation spaces, achieving modal alignment and fusion of molecules by combining molecular invariance and representation specificity. To enhance the interpretability and visualization capabilities of the model, graph attention mechanisms are introduced, enabling the identification and inference of important chemical fragments within molecules. Experimental results on publicly available cell line phenotype and kinase activity datasets demonstrate that MSSP outperforms the current state-of-the-art methods in molecular property prediction. Additionally, MSSP exhibits strong competitiveness across nine benchmark molecular property prediction datasets. Furthermore, in the task of predicting SRC kinase data properties, this study successfully screens promising therapeutic compounds from compound libraries by validating the predictions of the MSSP model and combining them with traditional methods such as molecular docking and molecular dynamics simulations. Multiple potential Lyn inhibitors have been discovered through this approach. The application of MSSP model is helpful to discover new molecules with new drug properties or functions, accelerate the process of drug discovery, save time and resources, and provide important guidance for drug discovery.