DNA structure has many potential places where endogenous compounds and xenobiotics can bind. Therefore, xenobiotics bind along the sites of the nucleic acid with the aim of changing its structure, its Show more
DNA structure has many potential places where endogenous compounds and xenobiotics can bind. Therefore, xenobiotics bind along the sites of the nucleic acid with the aim of changing its structure, its genetic message, and, implicitly, its functions. Currently, there are several mechanisms known to be involved in DNA binding. These mechanisms are covalent and non-covalent interactions. The covalent interaction or metal base coordination is an irreversible binding and it is represented by an intra-/interstrand cross-link. The non-covalent interaction is generally a reversible binding and it is represented by intercalation between DNA base pairs, insertion, major and/or minor groove binding, and electrostatic interactions with the sugar phosphate DNA backbone. In the present review, we focus on the types of DNAâmetal complex interactions (including some representative examples) and on presenting the methods currently used to study them. 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