The determination of efficient collective variables is crucial to the success of many enhanced sampling methods. As inspired by previous discrimination approaches, we first collect a set of data from the different metastable basins. The data are then projected with the help of a neural network into a low-dimensional manifold in which data from different basins are well-discriminated. This is here guaranteed by imposing that the projected data follows a preassigned distribution. The collective variables thus obtained lead to an efficient sampling and often allow reducing the number of collective variables in a multibasin scenario. We first check the validity of the method in two-state systems. We then move to multistep chemical processes. In the latter case, at variance with previous approaches, one single collective variable suffices, leading not only to computational efficiency but also to a very clear representation of the reaction free-energy profile.