Why do molecular generation models maximize “penalized logP” as a measure of drug-likeliness?
A positive logP is good (hydrophobic), but high results in ADMET problems (cytochromes break overly hydrophobic compounds) and aggregation (gunk, tar, crud are words happily thrown around). However, in most cases the logP is not greater than +3 because good binding is result of hydrogen bonds etc —some bad algorithms will suggest a polyaromatic hydrocarbon that makes lots of pi-pi interactions but these are few.
Confusingly, a high synthetic accessibility score is bad (hard). 3 is the average and 10 is basically something impossible. This score is how hard it is to make (i.e. pricier), which is one of the major drivers.
The number of cycles is number of rings. Even if you get pi-pi, cation-pi and sulfur-pi bonds you also get aggregation due to stacking (a crud). So is somewhat bad.
So basically that score is
high=good minus high=bad minus high=bad

If difference in relative Gibbs energy of binding is thrown it, negative ∆∆G is good, so that would be subtracted. As its atom count dependent ligand efficiency is better, but that ranges from 0 to -1.2 kcal/mol. So would need scaling.