## 1 Introduction  
The gastrointestinal adsorption of oral administered drugs is an essential parameter worth being investigated, as it influences the bioavailability and the effects that several medicines explicate on the body. Furthermore, among the ADME (absorption, distribution, metabolism, excretion) molecular properties, adsorption drives drug design significantly since bioavailability has proven to be one of the leading causes of drug discard during preclinical and phase I trials [1]. However, studying the permeability of drugs in real systems is a complex process, and not without practical obstacles and ethical problems. In this regard, Food and Drug Administration (FDA) guidelines include in vivo, in situ, and in vitro (tissues and cells such as Caco-2 cell) methods to assess drug permeability [2]. In this context, the parallel artificial membrane permeability assay (PAMPA) [3] has proven to be particularly advantageous in identifying promising candidates in the early stages of drug development, to the extent that it has been included in the Tier I ADME assay at the National Center for Advancing Translational Sciences (NCATS), together with rat liver microsomal (RLM) stability, and kinetic aqueous solubility [4]. Indeed, PAMPA is an inexpensive and easily reproducible non-cell permeability assay able to model passive transcellular permeation mechanisms, which are predominant in the gastrointestinal tract. Thus, even if PAMPA fails in simulating active and efflux transport, its outcomes correlate well with in vitro Caco-2 cell and in vivo human intestinal absorption, mainly because approximately 90% of Active pharmaceutical ingredients (APIs) are absorbed via passive diffusion [5]. Studies combining PAMPA and quantitative structure-activity relationship (QSAR) modeling are particularly topical, mainly due to the opportunity to predict gastrointestinal tract (GIT) adsorption and to interpret bioavailability in terms of molecular descriptors directly related to structural characteristics. Consequently, it is understandable how these studies represent a valuable contribution to drug development and risk assessment. Focusing attention on systems designed to simulate gastrointestinal tract absorption, most studies in this field apply regression methods (mainly multiple linear regression or partial least squares) on datasets derived from different PAMPA systems and specific to a certain class of bioactive compounds [6; 7; 8]. Otherwise, a very recent study concerning PAMPA-QSAR models, used to classify many pharmaceuticals into highly or poorly permeable classes, using random forest and graph convolutional neural networks as classification methods, has been proposed [4]. Surprisingly, most of these papers do not report a systematic study of pH; indeed, among the various parameters that can modify the permeability within the gastrointestinal tract, pH is certainly one of the most important [9; 10]. Medicaments can have a different ionic form (and consequently, a different capacity to be absorbed) as the pH varies. This chemical-physical criterion is not constant within the intestine, and it varies from lower (5.6 in the duodenum) to higher values (up to 8.5 in the colon). As a result, since drugs permeate the diverse intestinal tract differently, the entire range of intestinal pH should be tested for a reliable in vivo prediction. [PERSON] et al. [11] presented a well-designed dataset of acidic, basic, ampholytic, and neutral compounds which were analyzed over an extensive range of pH. This specific work aimed at developing a QSAR model, by applying best multilinear regression, to predict the pH permeability profiles of drug candidates. In addition, they recently reprocessed the dataset mentioned above to classify high- and low-permeable drug substances according to the Biopharmaceutical Classification System framework [2]. Firstly, a logistic regression method was applied to the different datasets, which are the outcomes of the different pH measurements.