We first generate the degrees of freedom corresponding to the local structure and then we sample the torsions, τ . The torsions are sampled through integration of the probability flow (neural) ODE (ordinary differential equation), corresponding to the torsional diffusion model, which further enables exact reweighting through sample likelihood calculation. We denote p 0 (τ ) the neural ODE sampler likelihood in torsional space. However, the Boltzmann distribution is generally specified as a function of the 3D coordinates of the atoms in a molecule, x. Therefore, to allow for compatibility with the Boltzmann measure, we need to express p 0 (τ ) in Euclidean space instead. As all the generated geometric quantities correspond to internal coordinates, and the local structure is generated deterministically, the Euclidean likelihood can be computed as