DTIBFAI: drug-target interaction prediction based on BERT and feature augment of Informer

Key Points

• Drug-target interaction predictions improved significantly due to the application of BERT and Informer feature augment.
• The model showed an increase in accuracy by over 15% on relevant benchmarks for drug-target predictions.
• Based on a hybrid approach incorporating deep learning and advanced data features, this method utilizes BERT for natural language processing tasks.
• Highlights the need for better prediction models in drug development, which may lead to more effective therapies.