## 2 Materials and Methods  
**Dataset collection and curation. ** The dataset was collected from ECOTOX database ([[https://cfpub.epa.gov/ecotox/index.cfm](https://cfpub.epa.gov/ecotox/index.cfm)]([https://cfpub.epa.gov/ecotox/index.cfm](https://cfpub.epa.gov/ecotox/index.cfm)), accessed on 6 June 2024) [15] with the following search parameters: (a) habitat: aquatic, (b) chemicals: Pharmaceuticals Personal Care Products (PPCPs), (c) Endpoints: LC (lethal concentration)/LD (lethal dose) xx (all % values) and EC (effective concentration)/ED (effective dose) xx (all % values). This dataset with 2520 entries was properly curated by setting a cut-off value of 5 mg/L, where the Conc 1 Units (Standardized) as Active Ingredient (AI) mg/L were designated as 'toxic' (Toxic\({}_{j}\) = +1), whereas the remaining values were selected as 'non-toxic' (Toxic\({}_{j}\) = 0). Therefore,  
Toxic\({}_{j}\) = +1, when Conc 1 Mean (Standardized) value \(\leq\) 5 mg/L  
Toxic\({}_{j}\) = 0, when Conc 1 Mean (Standardized) value \(>\) 5 mg/L  
Additionally, two endpoints--EC\({}_{50}\) and LC\({}_{50}\)--were considered for setting up the dataset for modeling. The decision regarding the selection of this cut-off value was guided by the guidelines of Globally Harmonized System of Classification and Labelling of Chemicals (GHS) of the United Nations for estimating toxic doses ([[https://unece.org/sites/default/files/2021-09/GHS_Rev9E_0.pdf](https://unece.org/sites/default/files/2021-09/GHS_Rev9E_0.pdf)]([https://unece.org/sites/default/files/2021-09/GHS_Rev9E_0.pdf](https://unece.org/sites/default/files/2021-09/GHS_Rev9E_0.pdf)), page 233, accessed on 2 June 2024). Here, if the LC\({}_{50}\) (96 h)/EC\({}_{50}\) (48 h) value of a chemical is found to be less than 1 mg/L, it comes under the 'Warning' or 'Acute 1' category for aquatic toxicity. In contrast, those chemicals with LC\({}_{50}\) (96 h)/EC\({}_{50}\) (48 h) values between 1 and 10 mg/L belong to the 'Acute 2' category. Therefore, a cut-off value of 5 mg/L may be selected so that most of the 'Acute 1' chemicals are assigned as \"toxic\" irrespectively of the experimental protocol. Generally, for any given cut-off value and experimental conditions, the ECOTOX dataset may contain several duplicated data points. Since mt-QSTR modeling was performed using the Box-Jenkins approach, it was essential to determine which experimental conditions should be included as factors for classification modeling. In this work, we used five experimental conditions, viz. (a) species scientific name (_sn_), (b) species group (_sg_), (c) concentration 1 type (standardized) (_co_), (d) endpoint (_ep_), and (e) time of exposure (_te_). Save for _te_, all other experimental elements were directly obtained from the downloadedECOTOX dataset. The _te_ element was derived from the exposure time [column name: 'Observed Duration Units (Days)' from ECOOTOX dataset], where datapoints with values between 0 and 4 days were designated as 'Short', values between 4 and 10 days were set as 'Medium', and values with greater than 10 days were denoted as 'Long'. The next challenge was identifying and processing duplicated data points. In conventional single-target QSTR, data points are considered duplicated when identical chemical structures appear multiple times with varying experimental results. Such duplicates can be easily identified by examining the canonical SMILES of the compounds in the dataset. However, in the case of mt-QSTR, the concept of a \"data point\" is more complex. Here, each data point represents a combination of a chemical structure and its associated experimental conditions included for modeling. Therefore, two data points can be considered duplicated only when both the canonical SMILES and the experimental conditions are identical. This complexity makes dataset curation for multi-target QSTR significantly more challenging, requiring extensive manual effort.