A Deep Multimodal Representation Learning Framework for Accurate Molecular Properties Prediction
Yuxin Yang, Zixu Wang, Pegah Ahadian, Abby Jerger, Jeremy Zucker, Song Feng, Feixiong Cheng, Qiang Guan · 2024
Drug discovery is a challenging process, requiring the optimization of compounds to become safe and effective. Predicting molecular properties is an indispensable step in the drug discovery pipeline. Traditionally, this process is costly, involving multiple rounds of experiments, rendering it impractical for every candidate compound. Deep learning techniques have emerged as a promising approach to drug discovery to reduce the cost during the process. However, prevalent research in deep learning models focused on predicting molecular properties has primarily fixated on single-modal models, neglecting the potential benefits of combining different data modalities. To overcome this limitation, we introduce MRL-Mol: a deep Multimodal Representation Learning framework for accurate Molecular properties prediction. MRL-Mol harnesses three data modalities: sequence, graph, and image, augmenting the depth of comprehension. Leveraging a large-scale unlabeled dataset ( 1M unique molecules), we pretrain MRL-Mol to extract inter- and intra-modal information. Our study demonstrates the superior performance of MRL-Mol in predicting molecular properties across six benchmark datasets. Notably, MRL-Mol outperforms other state-of-the-art molecular properties prediction models. These findings suggest that by combining information from multiple data modalities, MRL-Mol can comprehend molecules better than single-modal deep learning models and identify molecular properties with better accuracy.