  In recent years, pretraining models have made significant advancements in the
fields of natural language processing (NLP), computer vision (CV), and life
sciences. The significant advancements in NLP and CV are predominantly driven
by the expansion of model parameters and data size, a phenomenon now recognized
as the scaling laws. However, research exploring scaling law in molecular
pretraining models remains unexplored. In this work, we present Uni-Mol2 , an
innovative molecular pretraining model that leverages a two-track transformer
to effectively integrate features at the atomic level, graph level, and
geometry structure level. Along with this, we systematically investigate the
scaling law within molecular pretraining models, characterizing the power-law
correlations between validation loss and model size, dataset size, and
computational resources. Consequently, we successfully scale Uni-Mol2 to 1.1
billion parameters through pretraining on 800 million conformations, making it
the largest molecular pretraining model to date. Extensive experiments show
consistent improvement in the downstream tasks as the model size grows. The
Uni-Mol2 with 1.1B parameters also outperforms existing methods, achieving an
average 27% improvement on the QM9 and 14% on COMPAS-1D dataset.
