## 7 Conclusions and Recommendations  
Computer modeling is the most effective method for evaluating the compatibility between active compounds (ACs) and wall materials (WMs). It eliminates the need for time-consuming trial-and-error experiments and enhances our understanding of their chemical compatibility. Utilizing decoys is beneficial for preserving the precision of parameter and variable patterns in the presence of data constraints. Data purification is an essential and critical step in implementing statistical and Principal Component Analyses. It facilitates the identification of correlations between variables and the consolidation of data while retaining crucial information. This process is essential before model development. The developed model has proven its accuracy in compatibility assessments, highlighting the efficacy and capacity of this supervised learning approach to integrate new data. Applying model evaluation techniques like k-fold cross-validation and optimizing the number of neighbors improves the reliability and precision of the results. This approach explicitly targets concerns related to overfitting and robustness.  
Future research should prioritize incorporating a broader spectrum of molecular descriptors to ensure comprehensive model validation and accurate performance evaluation. Moreover, creating a publicly accessible tool would facilitate the evaluation of compound compatibility and enhance the dataset utilized to enhance the model. By exploring alternative supervised models and optimizing the hyperparameters of each model, one can gain deeper insights into determining compatibility. By utilizing different mathematical tools in the models, a more comprehensive understanding of the data can be achieved, allowing the discovery of additional relationships that need to be considered.  
Improvements in computing power and algorithm development are expected to reduce some of the current limitations, increasing the accessibility and reliability of these techniques [68]. Their predicted accuracy can be significantly improved by incorporating machine learning and artificial intelligence (AI) into molecular dynamics (MD) and docking. This is achieved by detecting patterns and correlations that may be overlooked by conventional approaches [69]. In the future, implementing molecular dynamics (MD) and docking for compatibility assessments will probably require a hybrid strategy that combines computational predictions with experimental validation. This integrated approach can exploit the advantages of both computational and empirical methodologies, providing a more thorough and effective way of evaluating compatibility. As computational techniques advance, they will become more valuable in assessing compatibility alongside traditional experimental methods and offering a greater understanding of molecular interactions.  
The databases of decoys, phenolic, and wall material registers used in this paper are available as supplementary materials in the online version [[https://www.mdpi.com/article/10.3390/a17090412/s1](https://www.mdpi.com/article/10.3390/a17090412/s1)]([https://www.mdpi.com/article/10.3390/a17090412/s1](https://www.mdpi.com/article/10.3390/a17090412/s1)). These databases include the smiles obtention, the compound name (phenolic and wall material databases), and the molecular descriptors obtention, with their corresponding links for further information.  
Conceptualization, J.Q.-R., R.A.-G. and N.R.; methodology, J.Q.-R. and R.A.-G.; software, J.Q.-R.; validation, J.Q.-R. and R.A.-G.; formal analysis, J.Q.-R.; resources, R.A.-G. and N.R.; data curation, J.Q.-R.; writing--original draft preparation, J.Q.-R. and R.A.-G.; writing--review and editing, J.Q.-R., R.A.-G. and N.R.; visualization, J.Q.-R. and R.A.-G.; supervision, R.A.-G. and N.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This work.  
The authors declare no conflict of interest.