BACKGROUND: Drug-target interactions (DTIs) are used to suggest new medications for diseases or reuse existing drugs to treat other diseases, since experimental procedures take years to complete and FDA (Food and Drug Administration) permission is necessary for drugs to be made available in the market. OBJECTIVE: Computational methods are favoured over wet lab experiments in drug analysis, considering that the process is tedious, time-consuming, and costly. The interactions between drug-targets are computationally identified paving the way for unknown drug-target interactions for numerous diseases unknown to researchers. METHODS: This paper presents Chronological Order based Wrapper Technique for Drug-target interaction prediction (CO-WT DTI) to discover novel DTI. In our proposed approach, drug features as well as protein features are obtained by three feature extraction techniques while dimensionality reduction is implemented to remove unfavourable features. The imbalance issue is taken care of by balancing methods while the performance of the proposed approach is validated on benchmark datasets. RESULTS: The proposed approach has been validated using four broadly used benchmark datasets, namely, GPCR (G protein-coupled receptors), Enzymes, Nuclear receptor, and Ion channels. Our experimental results outperform other state-of-the-art methods based on the AUC (Area under the Receiver Operating Characteristic (ROC) curve) metric and leave-one-out cross-validation (LOOCV) is used to evaluate the prediction performance of the proposed approach. CONCLUSION: The performance of feature extraction, balancing methods, dimensionality reduction and classifier suggest ways to contribute data towards anew drug advancement. It is anticipated that our model will help refine ensuing explorations, especially in the drug-target interaction domain.

Drug-target interactions prediction (DTIP) remains an important requirement in the field of drug discovery and human medicine. The identification of interaction among the drug compound and target protein plays an essential process in the drug discovery process. It is a lengthier and complex process for predicting the drug target interaction (DTI) utilizing experimental approaches. To resolve these issues, computational intelligence based DTIP techniques were developed to offer an efficient predictive model with low cost. The recently developed deep learning (DL) models can be employed for the design of effective predictive approaches for DTIP. With this motivation, this paper presents a new drug target interaction prediction using optimal recurrent neural network (DTIP-ORNN) technique. The goal of the DTIP-ORNN technique is to predict the DTIs in a semi-supervised way, i.e., inclusion of both labelled and unlabelled instances. Initially, the DTIP-ORNN technique performs data preparation process and also includes class labelling process, where the target interactions from the database are used to determine the final label of the unlabelled instances. Besides, drugto-drug (D-D) and target-to-target (T-T) interactions are used for the weight initiation of the RNN based bidirectional long short term memory (BiLSTM) model which is then utilized to the prediction of DTIs. Since hyperparameters significantly affect the prediction performance of the BiLSTM technique, the Adam optimizer is used which mainly helps to improve the DTI prediction outcomes. In order to ensure the enhanced predictive outcomes of the DTIP-ORNN technique, a series of simulations are implemented on four benchmark datasets. The comparative result analysis shows the promising performance of the DTIP-ORNN method on the recent approaches.