A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language

TL;DR

This paper introduces a multimodal AI model that links molecular graphs with natural language, improving understanding and prediction of molecular properties by leveraging both visual and textual data.

Contribution

It presents a novel contrastive learning framework that jointly pretrains on molecular graphs and related texts, bridging modalities for enhanced molecular understanding.

Findings

Improved performance in cross-modal retrieval and molecule captioning

Enhanced molecular property prediction accuracy

Capability to generate molecular graphs from natural language

Abstract

Although artificial intelligence (AI) has made significant progress in understanding molecules in a wide range of fields, existing models generally acquire the single cognitive ability from the single molecular modality. Since the hierarchy of molecular knowledge is profound, even humans learn from different modalities including both intuitive diagrams and professional texts to assist their understanding. Inspired by this, we propose a molecular multimodal foundation model which is pretrained from molecular graphs and their semantically related textual data (crawled from published Scientific Citation Index papers) via contrastive learning. This AI model represents a critical attempt that directly bridges molecular graphs and natural language. Importantly, through capturing the specific and complementary information of the two modalities, our proposed model can better grasp molecular expertise. Experimental results show that our model not only exhibits promising performance in cross-modal tasks such as cross-modal retrieval and molecule caption, but also enhances molecular property prediction and possesses capability to generate meaningful molecular graphs from natural language descriptions. We believe that our model would have a broad impact on AI-empowered fields across disciplines such as biology, chemistry, materials, environment, and medicine, among others.