  Coordinate denoising is a promising 3D molecular pre-training method, which
has achieved remarkable performance in various downstream drug discovery tasks.
Theoretically, the objective is equivalent to learning the force field, which
is revealed helpful for downstream tasks. Nevertheless, there are two
challenges for coordinate denoising to learn an effective force field, i.e. low
coverage samples and isotropic force field. The underlying reason is that
molecular distributions assumed by existing denoising methods fail to capture
the anisotropic characteristic of molecules. To tackle these challenges, we
propose a novel hybrid noise strategy, including noises on both dihedral angel
and coordinate. However, denoising such hybrid noise in a traditional way is no
more equivalent to learning the force field. Through theoretical deductions, we
find that the problem is caused by the dependency of the input conformation for
covariance. To this end, we propose to decouple the two types of noise and
design a novel fractional denoising method (Frad), which only denoises the
latter coordinate part. In this way, Frad enjoys both the merits of sampling
more low-energy structures and the force field equivalence. Extensive
experiments show the effectiveness of Frad in molecular representation, with a
new state-of-the-art on 9 out of 12 tasks of QM9 and on 7 out of 8 targets of
MD17.
