  The quest for accurate prediction of drug molecule properties poses a
fundamental challenge in the realm of Artificial Intelligence Drug Discovery
(AIDD). An effective representation of drug molecules emerges as a pivotal
component in this pursuit. Contemporary leading-edge research predominantly
resorts to self-supervised learning (SSL) techniques to extract meaningful
structural representations from large-scale, unlabeled molecular data,
subsequently fine-tuning these representations for an array of downstream
tasks. However, an inherent shortcoming of these studies lies in their singular
reliance on one modality of molecular information, such as molecule image or
SMILES representations, thus neglecting the potential complementarity of
various molecular modalities. In response to this limitation, we propose MolIG,
a novel MultiModaL molecular pre-training framework for predicting molecular
properties based on Image and Graph structures. MolIG model innovatively
leverages the coherence and correlation between molecule graph and molecule
image to execute self-supervised tasks, effectively amalgamating the strengths
of both molecular representation forms. This holistic approach allows for the
capture of pivotal molecular structural characteristics and high-level semantic
information. Upon completion of pre-training, Graph Neural Network (GNN)
Encoder is used for the prediction of downstream tasks. In comparison to
advanced baseline models, MolIG exhibits enhanced performance in downstream
tasks pertaining to molecular property prediction within benchmark groups such
as MoleculeNet Benchmark Group and ADMET Benchmark Group.
