Efficient machine learning model for predicting drug-target interactions with case study for Covid-19. (August 2021) - El-Behery, Heba; Attia, Abdel-Fattah; El-Fishawy, Nawal; Torkey, Hanaa
Graphical abstract: Highlights: Extract combined structured data (from the Drugbank) and the feature data (from benchmark data). Preprocessing the protein sequences and the drug's smile into a set of descriptors to convert sequences data into features. Apply these data on different machine learning techniques, deep learning techniques and ensemble learning techniques to predict interaction between the drugs and their target proteins in the human cell. Experimental comparison among these techniques on the extracted data reveal that results obtained by ensemble techniques like Light-Boost and Extra-tree were 98 % and F-Score 0.97 compared to 94 % and 0.92 achieved by current methods on either structure only of feature only dataset. Moreover, our model can predict more undetected interactions and thus can be used as a practical tool for drug repositioning. Our model is applied on the proteins known to be affected by COVID19 to predict possible interactions between these proteins the existing drugs announced in Drugbank. Which leads to discovering the drug reposition in the case of COVID19 infection which use the proteins affected by COVID19 in the human cell. Such as the ACE2 protein which interact with DB00691 and DB05203 with predicted probability of 100 %. Abstract: Background: Discover possible Drug Target Interactions (DTIs) is a decisive step in the detection of the effects of drugs as well as drug repositioning. There is a strong incentive to develop effective computational methods that can effectively predict potential DTIs, as traditional DTI laboratory experiments are expensive, time-consuming, and labor-intensive. Some technologies have been developed for this purpose, however large numbers of interactions have not yet been detected, the accuracy of their prediction still low, and protein sequences and structured data are rarely used together in the prediction process. Methods: This paper presents DTIs prediction model that takes advantage of the special capacity of the structured form of proteins and drugs. Our model obtains features from protein amino-acid sequences using physical and chemical properties, and from drugs smiles (Simplified Molecular Input Line Entry System) strings using encoding techniques. Comparing the proposed model with different existing methods under K-fold cross validation, empirical results show that our model based on ensemble learning algorithms for DTI prediction provide more accurate results from both structures and features data. Results: The proposed model is applied on two datasets: Benchmark (feature only) datasets and DrugBank (Structure data) datasets. Experimental results obtained by Light-Boost and ExtraTree using structures and feature data results in 98 % accuracy and 0.97 f-score comparing to 94 % and 0.92 achieved by the existing methods. Moreover, our model can successfully predict more yet undiscovered interactions, and hence can be used as a practical tool to drug repositioning. A case study of applying our prediction model on the proteins that are known to be affected by Corona viruses in order to predict the possible interactions among these proteins and existing drugs is performed. Also, our model is applied on Covid-19 related drugs announced on DrugBank. The results show that some drugs like DB00691 and DB05203 are predicted with 100 % accuracy to interact with ACE2 protein. This protein is a self-membrane protein that enables Covid-19 infection. Hence, our model can be used as an effective tool in drug reposition to predict possible drug treatments for Covid-19.