For each method, the torsion angles of all possible rotatable bonds in the molecule were used as model input variables and ranged from 0 to 2π. Torsion angle preferences have previously been derived by Guba et al. from commonly-occurring types of rotatable bonds observed in small molecule and protein-ligand X-ray crystal structures. We incorporated this prior knowledge into our Bayesian optimization algorithm using appropriately chosen kernels. Specifically, we used a locally periodic kernel, i.e. a product of a periodic kernel and a squared exponential kernel. This allowed us to model torsional potentials with varying amplitudes as well as different local minima and maxima . We derived the periodicity parameters for our kernels from the torsion potentials corresponding to 364 rotatable bond SMARTS patterns . The periodicity for each pattern is given in Additional File 1. Note that when the list of patterns did not cover a specific type of rotatable bond, we assigned general values for the periodicity parameter based on the atomic hybridization of the two atoms in the rotatable bond, i.e. sp 2 -sp 2 , sp 2 -sp 3 , and sp 3 -sp 3 .