Efficient machine learning model for predicting drug-target interactions with case study for Covid-19.

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.

Coronavirus disease 2019 (COVID-19): survival analysis using deep learning and Cox regression model.

Coronavirus (COVID-19) is one of the most serious problems that has caused stopping the wheel of life all over the world. It is widely spread to the extent that hospital places are not available for all patients. Therefore, most hospitals accept patients whose recovery rate is high. Machine learning techniques and artificial intelligence have been deployed for computing infection risks, performing survival analysis and classification. Survival analysis (time-to-event analysis) is widely used in many areas such as engineering and medicine. This paper presents two systems, Cox_COVID_19 and Deep_ Cox_COVID_19 that are based on Cox regression to study the survival analysis for COVID-19 and help hospitals to choose patients with better chances of survival and predict the most important symptoms (features) affecting survival probability. Cox_COVID_19 is based on Cox regression and Deep_Cox_COVID_19 is a combination of autoencoder deep neural network and Cox regression to enhance prediction accuracy. A clinical dataset for COVID-19 patients is used. This dataset consists of 1085 patients. The results show that applying an autoencoder on the data to reconstruct features, before applying Cox regression algorithm, would improve the results by increasing concordance, accuracy and precision. For Deep_ Cox_COVID_19 system, it has a concordance of 0.983 for training and 0.999 for testing, but for Cox_COVID_19 system, it has a concordance of 0.923 for training and 0.896 for testing. The most important features affecting mortality are, age, muscle pain, pneumonia and throat pain. Both Cox_COVID_19 and Deep_ Cox_COVID_19 prediction systems can predict the survival probability and present significant symptoms (features) that differentiate severe cases and death cases. But the accuracy of Deep_Cox_Covid_19 outperforms that of Cox_Covid_19. Both systems can provide definite information for doctors about detection and intervention to be taken, which can reduce mortality.