Inspired by the fitting of logistic regression classifiers, two parameters A and ω are used to best approximate our target function, namely p(S1→S0-E,x) as sampled from the corresponding occurrences observed from our MD runs. To this end, parameter A controls p(𝑥 = 〈𝑥〉) -which is reflected by the overall average of p(S1→S0-E). In turn, the parameter ω indicates how sharply the sigmoid shape of p(x) depends on the actual value x of the descriptor. This allows interpreting ω in terms of the suitability of a given descriptor model. For example, a poorly chosen descriptor that does not correlate at all with p(S1→S0-E,x) will essentially provide random numbers as inputs to p(x). The corresponding fit will thus show ω = 0 and p(S1→S0-E,x) = p(S1→S0-E) is a constant.