TC-DTA: predicting drug–target binding affinity with transformer and convolutional neural networks

Bioinformatics is a rapidly growing field involving the application of computational methods to the analysis and interpretation of biological data. An important task in bioinformatics is the identification of novel drug-target interactions (DTIs), which is also an important part of the drug discovery process. Most computational methods for predicting DTI consider it as a binary classification task to predict whether drug target pairs interact with each other. With the increasing amount of drug-target binding affinity data in recent years, this binary classification task can be transformed into a regression task of drug-target affinity (DTA), which reflects the degree of drug-target binding and can provide more detailed and specific information than DTI, making it an important tool in drug discovery through virtual screening. Effectively predicting how compounds interact with targets can help speed up the drug discovery process. In this study, we propose a deep learning model called TC-DTA for the prediction of the DTA, which makes use of the convolutional neural networks (CNN) and encoder module of the transformer architecture. First, the raw drug SMILES strings and protein amino acid sequences are extracted from the dataset. These are then represented using different encoding methods. We then use CNN and the Transformer's encoder module to extract feature information from drug SMILES strings and protein amino acid sequences, respectively. Finally, the feature information obtained is concatenated and fed into a multi-layer perceptron for prediction of the binding affinity score. We evaluated our model on two benchmark DTA datasets, Davis and KIBA, against methods including KronRLS, SimBoost and DeepDTA. On evaluation metrics such as Mean Squared Error, Concordance Index and r