## 1 Introduction  
Almost 30 years have passed since the discovery of the well-known Lipinski Rule of Five [1, 2]. It allows for quick resolution if a particular compound is likely to pose expected solubility and tissue permeability properties, which are essential in drug distribution. The main idea behind the Rule of Five (RO5) is to describe the compound with four simple molecular descriptors, namely the number of hydrogen bond (HB) donors, number of HB acceptors, molecular weight, and octanol-water partition coefficient (\(\log P\)). Based on experimental data, scientific intuition, and simple math, [PERSON] et al. formulated approximate, high-level binary descriptors as simple inequalities capturing expected druggability properties. In the classical form, the RO5 says that a compound is likely to have poor solubility and permeability properties if the following are met:  
* It contains more than five HB donors.
* It contains more than 10 HB acceptors.
* Its molecular weight \(>500\).
* Its octanol-water coefficient \(\log P>5.0\) (or Moriguchi \(M\log P>4.15\)).  
[PERSON] intended to predict whether the investigated compound will likely possess physiochemical properties, allowing for its successful distribution in the human circulatory system. From a practical perspective, it gives medicinal chemists an easily applicable tool capable of providing the first information about a compound's druggability. The simplicity of utilized molecular descriptors makes an overall computational cost low and thus is suitable for large-scale virtual screening procedures. In particular, the least promising candidates can be excluded before clinical trials.  
Besides the [PERSON] et al. contribution, nowadays, we know more than a dozen other similar rules. They involve various molecular descriptors, are based on slightly different assumptions, and are trained on varying data sets. The Ghose filter is an excellent example of another, well-established druggability index [3]. It also involves four molecular descriptors, but this time, they are water-octanol partition coefficient, molar refractivity, molecular weight, and the number of non-hydrogen atoms. [PERSON] et al. used the Comprehensive Medicinal Chemistry (CMC) database and found specific qualifying ranges of mentioned physiological properties covering at least 80% of the database compounds. They created several filter variants, depending on the drug class considered, e.g., anti-inflammatory, anti-depressant, or anti-hypertensive. In the literature, one can also find various drug-like scores [4; 5; 6; 7; 8; 9; 10; 11], lead-like scores [12; 13], the classification system BDDCS [14], and the exciting idea of Quantitative Estimate of Drug-Likeness (QED) [15]. Other efforts focused on the description the drug-likeness of molecules cover the application of Neural Network (NN) [16; 17; 18; 19; 20], Recursive Partitioning (RP) [21; 22], and Support Vector Machine (SVM) [17; 23].  
The primary aims of this paper are three-fold. First, we construct an original data set comprising a few thousand drug and non-drug compounds. Second, we demonstrate the applicability of a suitably adapted variant of DRSA to structure the data set and automatically infer simple, reproducible, and data-driven decision rules, reflecting druggability conditions or lack thereof. Third, we rigorously validate their predictive capabilities--captured by five performance metrics--against selected empirical rules and various DRSA approaches.