Discovering optimal reaction coordinates and accurately predicting kinetic rates for activated processes are two of the major challenges in molecular dynamics simulations. In this talk, we first address the challenge of transition rate prediction constructing accurate coarse-grained models of the dynamics of complex systems projected on a collective variable. We propose an algorithm for parameter estimation of Langevin equations from transition path sampling (or other unbiased) trajectories: by maximizing the model likelihood based on any explicit expression of the short-time propagator, this methodology efficiently reconstructs free energy landscapes, diffusion coefficients, and kinetic rates. Second, we use this algorithm and a variational principle that states that the optimal reaction coordinate is the one that minimizes the projected dynamics, to automatically optimize collective variables: we obtain Langevin models from linear combinations of trial collective variables and iteratively adjust and evaluate trial reaction coordinates until the model's rate is minimized. This framework provides quantitative predictions of kinetic rates and optimal reaction coordinates for both benchmarks and complex systems at a competitive computational cost.