Therapeutic activities of drugs are often influenced by co-administration of drugs that may cause inevitable drug-drug interactions (DDIs) and inadvertent side effects. Prediction and identification of DDIs are extremely vital for the patient safety and success of treatment modalities. A number of computational methods have been employed for the prediction of DDIs based on drugs structures and/or functions. Here, we report on a computational method for DDIs prediction based on functional similarity of drugs. The model was set based on key biological elements including carriers, transporters, enzymes and targets (CTET). The model was applied for 2189 approved drugs. For each drug, all the associated CTETs were collected, and the corresponding binary vectors were constructed to determine the DDIs. Various similarity measures were conducted to detect DDIs. Of the examined similarity methods, the inner product-based similarity measures (IPSMs) were found to provide improved prediction values. Altogether, 2,394,766 potential drug pairs interactions were studied. The model was able to predict over 250,000 unknown potential DDIs. Upon our findings, we propose the current method as a robust, yet simple and fast, universal in silico approach for identification of DDIs. We envision that this proposed method can be used as a practical technique for the detection of possible DDIs based on the functional similarities of drugs.

Application of computational methods in drug discovery has received increased attention in recent years as a way to accelerate drug target prediction. Based on 443 sequence-derived protein features, we applied the most commonly used machine learning methods to predict whether a protein is druggable as well as to opt for superior algorithm in this task. In addition, feature selection procedures were used to provide the best performance of each classifier according to the optimum number of features. When run on all features, Neural Network was the best classifier, with 89.98% accuracy, based on a k-fold cross-validation test. Among all the algorithms applied, the optimum number of most-relevant features was 130, according to the Support Vector Machine-Feature Selection (SVM-FS) algorithm. This study resulted in the discovery of new drug target which potentially can be employed in cell signaling pathways, gene expression, and signal transduction. The DrugMiner web tool was developed based on the findings of this study to provide researchers with the ability to predict druggable proteins. DrugMiner is freely available at www.DrugMiner.org.

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Introduction: The explosion of mobile phone users along with the importance of user’s role in managing their health provides a unique opportunity for m-Health applications in the management of chronic illnesses such as Multiple sclerosis (MS). Aim: To identify available MS applications and to characterize the content of MS self-management applications. Methods: Two popular online application stores (iTunes, Google play) were searched for multiple sclerosis -related apps using the following keywords: multiple sclerosis, disseminated multiple sclerosis, disseminated sclerosis, and MS. Apps were considered eligible if they had been customized only on multiple sclerosis. First, data was extracted from the description page for any eligible application. To achieve the study goal, the secondary analysis was performed only for self-management applications. Results: Search of two popular markets identified 1042 applications (747 applications from Google play, and 295 applications from iTunes). Of these, 104 unique applications met the inclusion criteria. Almost a quarter of eligible applications (26%) had been designed for multiple sclerosis self-management. Other purposes of the identified applications were diagnosing & treating (7.7%), doing tests (7.7%), connecting & communication for MS patients (4.8%), raising awareness of multiple sclerosis (15.4%), accessing to journals & news (6.7%), conferences & meetings (17.3%), supporting & donating to MS community (14.4%). Conclusion: It appears the mobile applications provide a multidimensional tool for patient with Multiple Sclerosis to improve their condition self-management. These applications can contribute to empowerment of the patients, and help their adherence to the therapeutic and management regimen of their conditions. Moreover, they can be utilized to collect information on the MS progress pattern in personal level for each individual patient. This information may provide health care professionals with evidence to help their patients toward enhancing self-management of their disease.