With the advancement of computer processing and the continuous updating of computing algorithms, the computational drug-target interaction prediction has shown the advantages of short time, low cost, high precision and wide range, which has received extensive attention a...
Kim et al. (2021) presented a survey of DL models in the prediction of drug–target interaction (DTI) and new medication development. They start by providing a thorough summary of many depictions of drugs and proteins, DL applications, and widely used exemplary data sets to test and train ...
Generally, there are two principle approaches for in silico prediction of drug–target interaction (DTI, also refered to as compound–protein interac- tions): docking simulations and machine learning methods [2]. In docking simulations, the 3D structure of drug molecules and targets are considered ...
and protein–pathway associations) is considered as an edge type. The KG stores the information in a triplet form where each triplet represents an interaction/association between two unique entities (e.g., aspirin, drug–target interaction, COX1). After constructing the KG infrastructure, we used...
Drug-target interaction (DTI) prediction has become a crucial prerequisite in drug design and drug discovery. However, the traditional biological experiment is time-consuming and expensive, as there are abundant complex interactions present in the large size of genomic and chemical spaces. For alleviat...
We achieve the state-of-the-art performance for a drug-target interaction (DTI) prediction task with a Transformer-based neural network model. By serializing SMILES, ngerprints and protein sequence data for pairs of compounds and proteins we achieved promising prediction of DTI. The model improves...
Drug-target interaction (DTI) prediction plays a crucial role in drug repositioning and virtual drug screening. Most DTI prediction methods cast the problem as a binary classification task to predict if interactions exist or as a regression task to predi
Drug repurposing is an exciting field of research toward recognizing a new FDA-approved drug target for the treatment of a specific disease. It has received extensive attention regarding the tedious, time-consuming, and highly expensive procedure with a high risk of failure of new drug discovery....
such as molecular property predictions3,4, drug–target interaction (DTI) predictions5,6,7,8,9,10,11and drug–drug interaction (DDI) predictions12,13. A key advantage to these methods is that deep learning algorithms can capture the complex nonlinear relationships between input and output data14...
Drug–target interaction (DTIs) prediction plays a vital role in probing new targets for breast cancer research. Considering the multifaceted challenges associated with experimental methods identifying DTIs, the in silico prediction of such interactions merits exploration. In this study, we develop a fea...