  Deep learning methods have been considered promising for accelerating
molecular screening in drug discovery and material design. Due to the limited
availability of labelled data, various self-supervised molecular pre-training
methods have been presented. While many existing methods utilize common
pre-training tasks in computer vision (CV) and natural language processing
(NLP), they often overlook the fundamental physical principles governing
molecules. In contrast, applying denoising in pre-training can be interpreted
as an equivalent force learning, but the limited noise distribution introduces
bias into the molecular distribution. To address this issue, we introduce a
molecular pre-training framework called fractional denoising (Frad), which
decouples noise design from the constraints imposed by force learning
equivalence. In this way, the noise becomes customizable, allowing for
incorporating chemical priors to significantly improve molecular distribution
modeling. Experiments demonstrate that our framework consistently outperforms
existing methods, establishing state-of-the-art results across force
prediction, quantum chemical properties, and binding affinity tasks. The
refined noise design enhances force accuracy and sampling coverage, which
contribute to the creation of physically consistent molecular representations,
ultimately leading to superior predictive performance.
