Computational Discovery of Putative Leads for Drug Repositioning through Drug-Target Interaction Prediction

Abstract

De novo experimental drug discovery is an expensive and time-consuming task. It requires the identification of drug-target interactions (DTIs) towards targets of biological interest, either to inhibit or enhance a specific molecular function. Dedicated computational models for protein simulation and DTI prediction are crucial for speed and to reduce the costs associated with DTI identification. In this paper we present a computational pipeline that enables the discovery of putative leads for drug repositioning that can be applied to any microbial proteome, as long as the interactome of interest is at least partially known. Network metrics calculated for the interactome of the bacterial organism of interest were used to identify putative drug-targets. Then, a random forest classification model for DTI prediction was constructed using known DTI data from publicly available databases, resulting in an area under the ROC curve of 0.91 for classification of out-of-sampling data. A drug-target network was created by combining 3,081 unique ligands and the expected ten best drug targets. This network was used to predict new DTIs and to calculate the probability of the positive class, allowing the scoring of the predicted instances. Molecular docking experiments were performed on the best scoring DTI pairs and the results were compared with those of the same ligands with their original targets. The results obtained suggest that the proposed pipeline can be used in the identification of new leads for drug repositioning. The proposed classification model is available at http://bioinformatics.ua.pt/software/dtipred/.

Computational prediction of drug–target interactions using chemogenomic approaches: an empirical survey

“…In [124], the BioLip [125] and BindingDB [126] databases are searched for interactions with a binding affinity < 10lM to be used as negatives.…”

Deep Neural Network Architecture for Drug-Target Interaction Prediction

“…We applied grid search for all the models in order to accurately compare and evaluate the performance. The descriptors used were the same as the original work [7], which contains a total of 432 protein descriptors and 323 drug descriptors, and collected using PyDPI package [2]. The protein descriptors are divided into amino acid composition, Moran autocorrelation and CTD (Composition, Transition, Distribution) descriptors.…”

“…We exploit the particular ability of CNNs to obtain 1D representations, which are features that express local dependencies or patterns, that can then be used in a Fully Connected Neural Network (FCNN), acting as a binary classifier. Coelho et al (2016) [7] dataset was used to evaluate and validate the model. Additionally, we compared our model with different approaches, specifically random forest (RF), a FCNN architecture and support vector machine (SVM).…”

Systematic drug repositioning through mining adverse event data in ClinicalTrials.gov

“…Typical approaches include high-throughput screening of marketed drugs (Qosa et al, 2016), targeted testing of a class of drugs for a new disease area (Wu et al, 2016a), and in silico methods (Hodos et al, 2016; Mullen et al, 2016), usually based on drug-target interactions (Coelho, Arrais & Oliveira, 2016; Zheng et al, 2015). …”