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Dimensionality reduction based multi-kernel framework for drug-target interaction prediction
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DOI：10.1016/j.chemolab.2021.104270.png)
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Abstract
En 中文
The prediction of novel drug-target interactions (DTIs) has intrinsic significance in drug discovery research. Wetlab experiments of DTIs are laborious and expensive; computational methods can help minimize the complexity of identifying unknown DTIs and accelerate the drug repositioning process. Nowadays, the number of drug-target features and their interactions regularly increases, disabling traditional computational methods? prediction and analyzing ability. Therefore, developing a new robust model to derive the reduced features for effective prediction is important. Further, accurate interactions also depend on the negative drug-target pairs, and it is worthwhile to build a technique to generate perfect negative pairs. To this end, we propose a new multi-label approach, called idti-MLKdr, by introducing multi-kernel learning (MKL) based SVM for DTIs prediction with various dimensionality reduction techniques. First, we have extracted the drug-target features from chemical structures and protein sequences, applying different feature extraction methods. A new technique has been developed to construct the negative drug-target pairs based on drug-drug (or protein-protein) similarity scores. Then, threedimensionality reduction techniques have been applied to the extracted drug-target features. Finally, we trained a multi kernel-based learner together with the reduced features and combined their prediction scores to show the final results. In this experiment, we considered auROC as an evaluation metric. The proposed method has been compared with the various existing methods under five-fold cross-validation, and the experimental results indicated that idti-MLKdr attains the best auROC for predicting DTIs. We believe that improved prediction performance will motivate the researchers for further drug development.
Keywords:
Drug-target interactions (DTIs)
Feature extraction
Molecular fingerprint
Feature selection
Data imbalance
Multi kernel learning
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