The MTE incorporates a collection of twenty-two conventional ML models, leveraging a versatile set of fingerprint features elaborated in Datastore section. Among them, the two most frequently utilized models are decision trees, namely random forest, and CatBoost. The MTE's suite of seventeen deep learning models encompasses a diverse range, spanning graph neural networks (GNNs), transformers, or a combination of multiple architectures within a single model. Specifically, for GNNs, our pipeline incorporates several message passing neural networks with a recursive neighborhood aggregation scheme, along with graph isomorphic neural networks (GIN) and graph convolutional neural networks. These GNNs transform the nodes and edges of molecular graph into feature vectors, capturing local neighbor information. For transformer models, the MTE employs MMDeacon, a language model pretrained on two languages (SMILES and IUPAC) using contrastive pretraining with large molecule datasets.