## 1 Introduction
[PERSON] et al. employed ML algorithms (MLR, KNN, and RF) to construct quantitative structure-activity relationship (QSAR) models for cruzain inhibitors. These models use molecular descriptors to forecast the biological activity of the inhibitors accurately [18]. [PERSON] et al. investigated the application of generative models, specifically variational autoencoders (VAEs), in creating new molecules and predicting their properties using QSAR models [19]. Using computational methods to anticipate the binding affinity between compounds and targets significantly increases the likelihood of identifying lead compounds by minimizing the need for wet-lab studies. ML and DL methods employing ligand-based and target-based strategies have been utilized to forecast binding affinities, resulting in time and cost savings in drug discovery endeavors. Nevertheless, ML is not limited to the pharmaceutical industry when predicting compatibility [20]. [PERSON] discovered that scientific research on the corrosion of aerosol canisters is extensive but inadequate, and only some of the physical and chemical principles can be used to predict corrosion accurately. Additionally, other possible issues are influenced by many parameters and formula components. [PERSON]'s paper utilized a data-driven methodology to address these restrictions and limits to forecast the compatibility between a novel product's formulation and packaging. The model exemplifies a classification method, a subset of supervised machine learning; it utilizes input values provided for training to derive a conclusion and produce an output that classifies a dataset into distinct categories [21]. [PERSON] et al. proposed a use case for ML in predicting compound compatibility. They assessed the functional similarity between natural compounds and approved drugs by combining various chemical similarity metrics and physiochemical properties using a machine learning approach [22]. This study exemplifies the parallels be tween the present and previous studies since both investigations employ machine learning approaches to examine chemical data and generate predictions. By incorporating a range of chemical similarity measures and physical features, the machine learning approach enables a thorough evaluation of compound compatibility. Therefore, considering a computational tool incorporating ML capabilities could be advantageous [23]. This work seeks a model with self-learning capabilities and compartmental adaptability of the prototype since ML is a data analysis model that automates the construction of analytic models. The K-nearest neighbors (KNN) algorithm is contemplated to construct a classification model that determines the compatibility between ACs and WMs, given its recognized capability in similar classification problems. To ascertain the compounds' eligibility for encapsulation, the model aims to assess the compatibility of the compounds by analyzing the relationships between their molecular descriptors. The paper is structured as follows: Section 2 involves gathering and organizing data. This process includes assessing two databases to analyze the connections between the encapsulation process and various antioxidants and encapsulant compounds. Afterward, the model parameters are determined using the RDKit cheminformatics toolkit to extract the molecular descriptors of each compound. A review of the existing research considers the associations between the compatibility and incompatibility between substances. To increase the variety of encapsulant chemicals and the number of possible combinations between them, the computational tool LIDEB's Useful Decoys (LUDe) is used to build decoys. Section 3 establishes the connections between ACs and WMs by identifying comparison and difference relations, which are determined throughout the cleaning process. Section 4 involves conducting statistical analysis to examine the interrelationships between variables using tools such as histograms, scatter plots, and Pearson and Kendall correlations.