## 2 Materials and Methods  
### MT-DTI Model and TRPV1 Inhibitor Hit Prediction  
The model was built based on pre-trained MT chemical and convolutional neural network (CNN) protein amino sequence representations. It takes as input molecular sequences of drugs and protein sequences of targets and outputs affinity score for their interaction. The MT-DTI model was trained with chemical-protein pairs combined and curated from the Drug Target Common and BindingDB databases to predict binding affinity (\(K_{D}\)) and inhibitory bioactivity (half maximal inhibitory concentration [IC\({}_{50}\)]) [14; 15]. After retrieving MT-DTI sources from a GitHub repository ([[https://github.com/deargen/mt-dti](https://github.com/deargen/mt-dti)]([https://github.com/deargen/mt-dti](https://github.com/deargen/mt-dti)), accessed on 14 April 2022), installation and implementation were performed following the guides provided in the repository. In brief, the Python Keras framework was used, and an 8-core TPU machine from Google Cloud was used to optimize the pre-training process. In the re-training of the main MT-DTI model, the pre-trained MT part was transferred to the main DTI for fine-tuning. To achieve optimal performance, the fine-tuning process was carried out using one of the most comprehensive DTI datasets, known as ChEMBL. ChEMBL is a highly regarded bioactivity database that has been curated to contain over 2.7 million compounds with bioactivity data against over 13,000 targets. These targets include a diverse range of proteins such as enzymes, ion channels, G protein-coupled receptors (GPCRs), and nuclear receptors. The inclusion of ChEMBL in the fine-tuning process ensures that the MT-DTI model is highly accurate and effective in predicting drug-target interactions. For in silico screening, a catalog of 407,270 natural products was obtained from the Collection of Open Natural ProUcTs (COCONUT) database [16]. Compounds with molecular weights (MW) in the range of 380-800 g/mol were used to predict their binding affinity for the target protein. Compounds in the COCONUT DB with MWs lower than 380 g/mol (100 of 407,270 compounds) were excluded due to their low annotation levels and natural compound likeness scores. Compounds with MWs higher than 800 g/mol were rare in the model training dataset; thus, an MW cut-off of <800 g/mol was set to reduce uncertainty. The drug-target interaction deep learning MT-DTI model was used to predict the \(K_{D}\) and IC\({}_{50}\) of the sequence library [17]. The MT-DTI model predicted the effective \(K_{D}\) or IC\({}_{50}\) values for the chemical (SMILES) and amino acid sequences (FASTA) of the target protein (Figure 1) with the TRPV1 amino acid sequence (UniProt ID Q8 NER1) as input. The results were screened for compounds with \(K_{D}<500\) nM and classified into three groups based on the pharmacophore feature similarities of known TRPV1 antagonists as center compounds, including SB452533, JTS-653, or maxtrep, to increase the chance of successful hit identification (Table 1). Although IC\({}_{50}\) values were also predicted, the results were not used for screening due to the relatively small size of the training dataset compared to that of the \(K_{D}\)-based DTI model. The pharmacophore features were calculated using an open-source cheminformatics RDkit ([[https://www.rdkit.org](https://www.rdkit.org)]([https://www.rdkit.org](https://www.rdkit.org)), accessed on 17 April 2022) and exported in 1575 bit-vector formats. Each bit refers to a particular combination of feature types (H-donor, H-acceptor, negative-ionizable, positive-ionizable, aromatic ring, and lumped-hydrophobic) and the hop distance between each feature type. The 1575-dimensional pharmacophore feature was analyzed in three dimensions via a principal component analysis (PCA), and molecules with similar features were grouped via K-means clustering [18; 19].