## 4 Conclusions  
In this work, the Box-Jenkins-based moving average approach was adopted to set up validated and predictive mt-QSTR models. Linear and non-linear models were developed using a range of feature selection algorithms and machine learning techniques. Notably, it is the LDA technique that produced the most statistically significant model (L08), which outperformed both non-linear and subsequent consensus models.  
The linear model clearly demonstrated the contributions of various deviation descriptors. The PPCPs in the current dataset cover a large chemo-biological space, and their complexity is reflected in the L08 model, where complex graph-based topological descriptors predominate over simple, interpretable descriptors. It is well known that factors such as lipophilicity and water solubility of the compounds play crucial roles in the aquatic toxicity of chemicals. Indeed, our linear model suggests that intrinsic factors, such as mass and van der Waals volume of the atoms, play pivotal roles.  
Closely related descriptors (e.g., \(\Delta\)(MATS7v)\({}_{sn}\) and \(\Delta\)(MATS8v)\({}_{sn}\)) were found to have opposite relationships with the response variable. Nevertheless, the descriptor with the maximum contribution was \(\Delta\)(Ms)\({}_{sn}\). The model indicates the contribution of lipophilicity in the form of the descriptor \(\Delta\)(XLOGP2)\({}_{sn}\) and explains that the presence of heavy atoms, such as Cl and I, increases the risk of environmental toxicity. Similarly, low molecular weight PPCPs with an increased number of electronegative elements (N, O, etc.) have less potential to elicit aquatic toxicity.  
Clearly, the developed model can be used to predict the aquatic toxicity of new PPCPs and help design new PPCPs with low environmental toxicity. This work not only focuses on model generation but also paves the way for dataset curation in an automated fashion with the help of our _in-house_ software, Ecotox-curator, which may be employed in the future for performing mt-QSTR modeling.  
The following supporting information can be downloaded at [[https://www.mdpiom/xxx/s1](https://www.mdpiom/xxx/s1)]([https://www.mdpiom/xxx/s1](https://www.mdpiom/xxx/s1)); Table S1: List of the curated data points utilized for model development and validation; Table S2: Hyperparameters optimized for generating machine learning models. Table S3: Possible consensus models, resulting from a combination of the several developed models.  
Conceptualization, A.K.H. and M.N.D.S.C.; formal analysis, A.K.H. and T.P.; data curation, A.K.H. and T.P.; funding acquisition, M.N.D.S.C.; investigation, A.K.H., T.P. and M.N.D.S.C.; methodology, A.K.H. and M.N.D.S.C.; software, A.K.H. and M.N.D.S.C.; project administration, M.N.D.S.C.; supervision, A.K.H. and M.N.D.S.C.; validation, A.K.H., T.P. and M.N.D.S.C.; writing--original draft, A.K.H. and T.P.; writing--review and editing, A.K.H. and M.N.D.S.C. All authors have read and agreed to the published version of the manuscript.  
This work received financial support from the PT national funds (FCT/MCTES, Fundacao para a Ciencia e Tecnologia and Ministerio da Ciencia, Tecnologia e Ensino Superior) through the project UID/50006--Laboratorio Associado para a Quimica Verde--Tecnologias e Processos Limpos.  
Not applicable.  
The original contributions presented in this study are included in the article/Supplementary Materials. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.