  While molecular pre-training has shown great potential in enhancing drug
discovery, the lack of a solid physical interpretation in current methods
raises concerns about whether the learned representation truly captures the
underlying explanatory factors in observed data, ultimately resulting in
limited generalization and robustness. Although denoising methods offer a
physical interpretation, their accuracy is often compromised by ad-hoc noise
design, leading to inaccurate learned force fields. To address this limitation,
this paper proposes a new method for molecular pre-training, called sliced
denoising (SliDe), which is based on the classical mechanical intramolecular
potential theory. SliDe utilizes a novel noise strategy that perturbs bond
lengths, angles, and torsion angles to achieve better sampling over
conformations. Additionally, it introduces a random slicing approach that
circumvents the computationally expensive calculation of the Jacobian matrix,
which is otherwise essential for estimating the force field. By aligning with
physical principles, SliDe shows a 42\% improvement in the accuracy of
estimated force fields compared to current state-of-the-art denoising methods,
and thus outperforms traditional baselines on various molecular property
prediction tasks.
