force," in the ordinary sense as used in the literature on liquids and solutions. Moreover, this analogy does not solve the issue of how to specify a suitable reference state for proteins. Machine learning In the mid-2000s, authors started to combine multiple statistical potentials, derived from different structural features, into composite scores. For that purpose, they used machine learning techniques, such as support vector machines (SVMs). Probabilistic neural networks (PNNs) have also been applied for the training of a position-specific distance-dependent statistical potential. In 2016, the DeepMind artificial intelligence research laboratory started to apply deep learning techniques to the development of a torsion- and distance-dependent statistical potential. The resulting method, named AlphaFold, won the 13th Critical Assessment of Techniques for Protein Structure Prediction (CASP) by correctly predicting the most accurate structure for 25 out of 43 free modelling domains. Explanation Bayesian probability Baker and co-workers justified statistical PMFs from a Bayesian point of view and used these insights in the construction of the coarse grained ROSETTA energy function. According to Bayesian probability calculus, the conditional probability of a structure , given the amino acid sequence , can be written as: is proportional to the product of the likelihood times the prior . By assuming that the likelihood can be approximated as a product of pairwise probabilities, and applying Bayes' theorem, the likelihood can be written as: where the product runs over all amino acid pairs (with ), and is the distance between amino acids and . Obviously, the negative of the logarithm of the expression has the same functional form as the classic pairwise distance statistical PMFs, with the denominator playing the role of the reference state. This explanation has two shortcomings: it relies on the unfounded assumption the likelihood can be expressed as a product of pairwise probabilities, and it is purely qualitative. Probability kinematics Hamelryck and co-workers later gave a quantitative explanation for the statistical potentials, according to which they approximate a form of probabilistic reasoning due to Richard Jeffrey and named probability kinematics. This variant of Bayesian thinking (sometimes called "Jeffrey conditioning") allows updating a prior distribution based on new information on the probabilities of the elements of a partition on the support of the prior. From this point of view, (i) it is not necessary to assume that the database of protein structures—used to build the potentials—follows a Boltzmann distribution, (ii) statistical potentials generalize readily beyond