Multi-to-uni modal knowledge transfer pre-training for molecular representation learning.

The pre-training molecular representation learning (MRL) has shown considerable potential in computer-aided drug discovery. Recently, many multimodal pre-training MRL methods have been presented, incorporating multimodal molecular data for pre-training and achieving high-accuracy predictions in downstream tasks. However, most current methods require completeness of modality for molecular data in the pre-training phase and often overlook their adaptation to real-world scenarios where, for example, molecular modalities except 2D topological graphs (2D modality) are often unavailable. In this study, we propose a multimodal pre-training MRL framework called M2UMol, which separately matches 2D modality to multiple modalities and undergoes pre-training jointly with a modality classifier. In this way, M2UMol elegantly transfers multimodal knowledge into the 2D modal encoder and allows for inputting incomplete modalities in the pre-training stage. Moreover, in downstream tasks with only the 2D modality given, M2UMol enables the precise simulation of molecular multimodal information based on the pre-trained 2D modal encoder. Comprehensive experimental results show the superior performance of M2UMol in a wide range of molecular tasks with higher efficiency in pre-training than pioneer models and demonstrate the validity of the multimodal knowledge transfer. Furthermore, we developed a user-friendly package based on M2UMol, integrating molecular representation learning, key functional group analysis, molecular multimodal retrieval, etc. It may be conveniently used in diverse fields related to drug discovery and promises to facilitate the process of developing drugs. Our code, pre-trained weights of M2UMol, and the package are available at https://github.com/Zhankun-Xiong/M2UMol .