Integrating Chemical Language and Molecular Graph in Multimodal Fused Deep Learning for Drug Property Prediction

TL;DR

This paper introduces a multimodal deep learning approach combining chemical language and molecular graphs to improve drug property prediction, demonstrating enhanced accuracy, robustness, and generalization over mono-modal methods.

Contribution

The study presents a novel triple-modal fusion framework integrating SMILES, ECFP, and molecular graphs with multiple fusion strategies, advancing molecular property prediction capabilities.

Findings

Outperforms mono-modal models in accuracy and noise resistance

Demonstrates strong generalization in protein-ligand binding prediction

Uses diverse data sources to enhance model robustness

Abstract

Accurately predicting molecular properties is a challenging but essential task in drug discovery. Recently, many mono-modal deep learning methods have been successfully applied to molecular property prediction. However, the inherent limitation of mono-modal learning arises from relying solely on one modality of molecular representation, which restricts a comprehensive understanding of drug molecules and hampers their resilience against data noise. To overcome the limitations, we construct multimodal deep learning models to cover different molecular representations. We convert drug molecules into three molecular representations, SMILES-encoded vectors, ECFP fingerprints, and molecular graphs. To process the modal information, Transformer-Encoder, bi-directional gated recurrent units (BiGRU), and graph convolutional network (GCN) are utilized for feature learning respectively, which can enhance the model capability to acquire complementary and naturally occurring bioinformatics information. We evaluated our triple-modal model on six molecule datasets. Different from bi-modal learning models, we adopt five fusion methods to capture the specific features and leverage the contribution of each modal information better. Compared with mono-modal models, our multimodal fused deep learning (MMFDL) models outperform single models in accuracy, reliability, and resistance capability against noise. Moreover, we demonstrate its generalization ability in the prediction of binding constants for protein-ligand complex molecules in the refined set of PDBbind. The advantage of the multimodal model lies in its ability to process diverse sources of data using proper models and suitable fusion methods, which would enhance the noise resistance of the model while obtaining data diversity.