## 4 Conclusions  
The aim of this work was the use of AI/ML tools for an empirical data analysis of the mutual interactions of water with hydroxypropyl cellulose HPC. AI/ML potential to broaden knowledge was employed to unravel some hidden relationships concerning thermal and ionic phenomena. We were able to confirm the assisting role of AI/ML in the formulation of hypotheses and their at least partial verification and/or falsification in relation to the physical phenomena observed with commonly applicable analytical methods--in our case, a DSC assay. The main conclusions of the paper are:  
* assigning the maximum temperatures Tmax to the empirically determined components allowed confirming the identity of each water fraction--the same conclusions regarding the relationship Tmax vs. Wc, but obtained from raw, not decomposed DSC curves, were the subject of another publication [20]
* it was shown that the location of the components determined during AI/ML analysis was justified by the Raman spectrometry method [36; 37]
* as a result of the method used, the effects of drugs with different solubilities were very well differentiated and shown. The location of the components as well as quantitative aspects were confirmed in the DSC experiments carried out earlier [20; 21; 38]
* a significant contribution of nanocavities in the formation of strongly bound non-freezing water (NFW) was indicated--the obtained results were confirmed in previous publications [18; 20; 21; 29; 37; 39]
* it was pointed out the influence of strongly dissociating Na+ ions on some quantitative relationships associated with strongly bound non-freezing water (NFW) [20; 40]  
The presented three-component analysis met our all assumptions and expectations. The obtained theoretical model confirmed the physical properties of hydrated HPC--both raw and in a mixture with drugs of different solubility--previously obtained using differential scanning calorimetry DSC. Such results provide a very promising and proven instrument for modelling hydrated polysaccharide systems, also mixed with small-molecule drugs with varying solubility. The obtained results encourage to perform a decomposition analysis using the same algorithm on polymers with a different structure and physicochemical properties, also in mixtures with macromolecular drugs or proteins. This type of research may contribute to a better understanding of the release of both highly and poorly soluble drugs from tablet formulations and their dissolution kinetics. Moreover, based on the well-known biopharmaceutical paradigm of drugs fate in the body LADME, the identification of the driving forces and mechanisms of drug release is crucial to the understanding and prediction of drugs' bioavailability. The latter is a primary endpoint for many types of clinical trials and plays a pivotal role in the assessment of drugs' efficacy and safety--an obligatory assessment required for every drug introduced to the market. The major achievements and novelty of this work include:  
* the development and publication of the AI/ML tool for the decomposition of DSC curves as an open source software freely available both for personal and for commercial use [28]the proof-of-concept use of the above-mentioned tool for DSC results for HPC/water mixtures with model drugs
* the confirmation of some AI/ML empirically developed hypotheses in the literature about the thermal behavior of HPC/water mixtures  
Conceptualization, P.T. and A.M.; methodology, P.T. and A.M.; software, A.M.; formal analysis, P.T. ; investigation, A.M.; data curation, P.T. and A.M.; writing--original draft preparation, P.T. and A.M.; writing--review and editing, P.T. ; visualization, P.T. and A.M. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Software: R peak decomposer and Database (database.zip).