Abstract
Non-small cell lung cancer (NSCLC) accounts for the majority of lung cancer cases, making it the most fatal diseases worldwide. Predicting NSCLC patients' survival outcomes accurately remains a significant challenge despite advancements in treatment. The difficulties in developing effective drug therapies, which are frequently hampered by severe side effects, drug resistance, and limited effectiveness across diverse patient populations, highlight the complexity of NSCLC. The machine learning (ML) and deep learning (DL) modelsare starting to reform the field of NSCLC drug disclosure. These methodologies empower the distinguishing proof of medication targets and the improvement of customized treatment techniques that might actually upgrade endurance results for NSCLC patients. Using cutting-edge methods of feature extraction and transfer learning, we present a drug discovery model for the identification of therapeutic targets in this paper. For the purpose of extracting features from drug and protein sequences, we make use of a hybrid UNet transformer. This makes it possible to extract deep features that address the issue of false alarms. For dimensionality reduction, the modified Rime optimization (MRO) algorithm is used to select the best features among multiples. In addition, we design the deep transfer learning (DTransL) model to boost the drug discovery accuracy for NSCLC patients' therapeutic targets. Davis, KIBA, and Binding-DB are examples of benchmark datasets that are used to validate the proposed model. Results exhibit that the MRO+DTransL model outflanks existing cutting edge models. On the Davis dataset, the MRO+DTransL model performed better than the LSTM model by 9.742%, achieved an accuracy of 98.398%. It reached 98.264% and 97.344% on the KIBA and Binding-DB datasets, respectively, indicating improvements of 8.608% and 8.957% over baseline models.