  The task of deducing three-dimensional molecular configurations from their
two-dimensional graph representations holds paramount importance in the fields
of computational chemistry and pharmaceutical development. The rapid
advancement of machine learning, particularly within the domain of deep
generative networks, has revolutionized the precision of predictive modeling in
this context. Traditional approaches often adopt a two-step strategy: initially
estimating interatomic distances and subsequently refining the spatial
molecular structure by solving a distance geometry problem. However, this
sequential approach occasionally falls short in accurately capturing the
intricacies of local atomic arrangements, thereby compromising the fidelity of
the resulting structural models. Addressing these limitations, this research
introduces a cutting-edge generative framework named \method{}. This framework
is grounded in the principles of diffusion observed in classical
non-equilibrium thermodynamics. \method{} views atoms as discrete entities and
excels in guiding the reversal of diffusion, transforming a distribution of
stochastic noise back into coherent molecular structures through a process akin
to a Markov chain. This transformation commences with the initial
representation of a molecular graph in an abstract latent space, culminating in
the realization of three-dimensional structures via a sophisticated bilevel
optimization scheme meticulously tailored to meet the specific requirements of
the task. One of the formidable challenges in this modeling endeavor involves
preserving roto-translational invariance to ensure that the generated molecular
conformations adhere to the laws of physics. Extensive experimental evaluations
confirm the efficacy of the proposed \method{} in comparison to
state-of-the-art methods.
