  We present a novel way to predict molecular conformers through a simple
formulation that sidesteps many of the heuristics of prior works and achieves
state of the art results by using the advantages of scale. By training a
diffusion generative model directly on 3D atomic positions without making
assumptions about the explicit structure of molecules (e.g. modeling torsional
angles) we are able to radically simplify structure learning, and make it
trivial to scale up the model sizes. This model, called Molecular Conformer
Fields (MCF), works by parameterizing conformer structures as functions that
map elements from a molecular graph directly to their 3D location in space.
This formulation allows us to boil down the essence of structure prediction to
learning a distribution over functions. Experimental results show that scaling
up the model capacity leads to large gains in generalization performance
without enforcing inductive biases like rotational equivariance. MCF represents
an advance in extending diffusion models to handle complex scientific problems
in a conceptually simple, scalable and effective manner.
