The synthesis of triazoles has attracted a lot of interest in the field of organic chemistry because of its versatile chemical characteristics and possible biological uses. This review offers an exten Show more
The synthesis of triazoles has attracted a lot of interest in the field of organic chemistry because of its versatile chemical characteristics and possible biological uses. This review offers an extensive overview of the different pathways used in the production of triazoles. A detailed analysis of recent research indicates that triazole compounds have a potential range of pharmacological activities, including the ability to inhibit enzymes, and have antibacterial, anticancer, and antifungal activities. The integration of computational and experimental methods provides a thorough understanding of the structure–activity connection, promoting sensible drug design and optimization. By including triazoles as essential components in drug discovery, researchers can further explore and innovate in the synthesis, biological assessment, and computational studies of triazoles as drugs, exploring the potential therapeutic significance of triazoles. Graphical abstract Show less
The task of drug-target interaction prediction holds significant importance in pharmacology and therapeutic drug design. In this paper, we present FRnet-DTI, an auto-encoder based feature manipulation Show more
The task of drug-target interaction prediction holds significant importance in pharmacology and therapeutic drug design. In this paper, we present FRnet-DTI, an auto-encoder based feature manipulation and a convolutional neural network based classifier for drug target interaction prediction. Two convolutional neural networks are proposed: FRnet-Encode and FRnet-Predict. Here, one model is used for feature manipulation and the other one for classification. Using the first method FRnet-Encode, we generate 4096 features for each of the instances in each of the datasets and use the second method, FRnet-Predict, to identify interaction probability employing those features. We have tested our method on four gold standard datasets extensively used by other researchers. Experimental results shows that our method significantly improves over the state-of-the-art method on three out of four drug-target interaction gold standard datasets on both area under curve for Receiver Operating Characteristic (auROC) and area under Precision Recall curve (auPR) metric. We also introduce twenty new potential drug-target pairs for interaction based on high prediction scores. The source codes and implementation details of our methods are available from https://github.com/farshidrayhanuiu/FRnet-DTI/ and also readily available to use as an web application from http://farshidrayhan.pythonanywhere.com/FRnet-DTI/ . Show less