# Why do molecular generation models maximize “penalized logP” as a measure of drug-likeliness?

I found that Lipinski's rule of five states that Log P (octanol-water partition coefficient, lipophilicity measure) usually should not exceed 5.

Many papers about drug discovery machine learning models tell about maximization of "penalized logP", but the paper they refer in the end does not contain any information on it. Let me show that.

The second task is to produce novel molecules with desired properties. Following (Kusner et al., 2017), our target chemical property y(·)is octanol-water partition coefficients (logP) penalized by the synthetic accessibility (SA)score and number of long cycles

y(m) = logP(m)−SA(m)−cycle(m)


where cycle(m)counts the number of rings that have more than six atoms.

Then we go into Kusner et al., 2017:

For the second optimization problem, we follow (Gómez-Bombarelli et al., 2016b) and optimize the drug properties of molecules. Our goal is to maximize the water-octanol partition coefficient (logP), an important metric in drug design that characterizes the drug-likeness of a molecule.As in Gómez-Bombarelli et al. (2016b) we consider a penalized logP score that takes into account other molecular properties such as ring size and synthetic accessibility (Ertl & Schuffenhauer, 2009).

Here is Gómez-Bombarelli, 2016 and it seems to contain nothing on the topic (I can be mistaken, please point me then), except

The objective we chose to optimize was 5×QED−SAS, where QED is the Quantitative Estimation of Drug-likeness (QED),37 and SAS is the Synthetic Accessibility score.36Thisobjective represents a rough estimate of finding the most drug-like molecule that is also easy to synthesize.

Other examples of the papers also leading to the same papers are 2, 3, etc.

What does penalized logP mean for drug-likeliness and why it should be maximized?

high=good minus high=bad minus high=bad