Deep Learning-Based Potential Ligand Prediction Framework for COVID-19 with Drug–Target Interaction Model
Sign Up to like & getrecommendations! Published in 2021 at "Cognitive Computation"
DOI: 10.1007/s12559-021-09840-x
Abstract: To fight against the present pandemic scenario of COVID-19 outbreak, medication with drugs and vaccines is extremely essential other than ventilation support. In this paper, we present a list of ligands which are expected to… read more here.
Keywords: drug; list ligands; drug target; target interaction ... See more keywords
A kernel matrix dimension reduction method for predicting drug-target interaction
Sign Up to like & getrecommendations! Published in 2017 at "Chemometrics and Intelligent Laboratory Systems"
DOI: 10.1016/j.chemolab.2017.01.016
Abstract: Abstract The prediction of drug-target interactions plays an important role in the drug discovery process, which serves to identify new drugs or novel targets for existing drugs. However, experimental methods for predicting drug-target interactions are… read more here.
Keywords: predicting drug; kernel matrix; target interaction; drug ... See more keywords
BE-DTI': Ensemble framework for drug target interaction prediction using dimensionality reduction and active learning
Sign Up to like & getrecommendations! Published in 2018 at "Computer methods and programs in biomedicine"
DOI: 10.1016/j.cmpb.2018.08.011
Abstract: BACKGROUND AND OBJECTIVE Drug-target interaction prediction plays an intrinsic role in the drug discovery process. Prediction of novel drugs and targets helps in identifying optimal drug therapies for various stringent diseases. Computational prediction of drug-target… read more here.
Keywords: drug; drug target; prediction; target interaction ... See more keywords
FRnet-DTI: Deep convolutional neural network for drug-target interaction prediction
Sign Up to like & getrecommendations! Published in 2020 at "Heliyon"
DOI: 10.1016/j.heliyon.2020.e03444
Abstract: 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… read more here.
Keywords: drug; drug target; target interaction; frnet ... See more keywords
Predicting Drug-Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation
Sign Up to like & getrecommendations! Published in 2019 at "Journal of chemical information and modeling"
DOI: 10.1021/acs.jcim.9b00387
Abstract: We propose a novel deep learning approach for predicting drug-target interaction using a graph neural network. We introduce a distance-aware graph attention algorithm to differentiate various types of intermolecular interactions. Furthermore, we extract the graph… read more here.
Keywords: predicting drug; drug target; target interaction;
A comparative chemogenic analysis for predicting Drug-Target Pair via Machine Learning Approaches
Sign Up to like & getrecommendations! Published in 2020 at "Scientific Reports"
DOI: 10.1038/s41598-020-63842-7
Abstract: A computational technique for predicting the DTIs has now turned out to be an indispensable job during the process of drug finding. It tapers the exploration room for interactions by propounding possible interaction contenders for… read more here.
Keywords: target interaction; target; drug; drug target ... See more keywords
Chiral carbon dots - a functional domain for tyrosinase Cu active site modulation via remote target interaction.
Sign Up to like & getrecommendations! Published in 2022 at "Nanoscale"
DOI: 10.1039/d1nr07236f
Abstract: The nano-hybrid enzyme is an ideal catalytic system that integrates various advantages from biocatalysis and nanocatalysis into homogeneous and heterogeneous catalysis. However, great efforts are still needed to fully understand the interactions between nanoparticles and… read more here.
Keywords: via remote; chiral carbon; remote target; functional domain ... See more keywords
AttentionSiteDTI: an interpretable graph-based model for drug-target interaction prediction using NLP sentence-level relation classification
Sign Up to like & getrecommendations! Published in 2022 at "Briefings in Bioinformatics"
DOI: 10.1093/bib/bbac272
Abstract: Abstract In this study, we introduce an interpretable graph-based deep learning prediction model, AttentionSiteDTI, which utilizes protein binding sites along with a self-attention mechanism to address the problem of drug–target interaction prediction. Our proposed model… read more here.
Keywords: drug target; drug; target interaction; prediction ... See more keywords
Predicting Drug-target Interaction Via Self-supervised Learning.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE/ACM transactions on computational biology and bioinformatics"
DOI: 10.1109/tcbb.2022.3153963
Abstract: Recent advances in graph representation learning provide new opportunities for computational drug-target interaction (DTI) prediction. However, it still suffers from deficiencies of dependence on manual labels and vulnerability to attacks. Inspired by the success of… read more here.
Keywords: self supervised; drug; interaction; target interaction ... See more keywords
Drug-target interaction prediction using semi-bipartite graph model and deep learning
Sign Up to like & getrecommendations! Published in 2020 at "BMC Bioinformatics"
DOI: 10.1186/s12859-020-3518-6
Abstract: Background Identifying drug-target interaction is a key element in drug discovery. In silico prediction of drug-target interaction can speed up the process of identifying unknown interactions between drugs and target proteins. In recent studies, handcrafted… read more here.
Keywords: target interaction; target; drug; model ... See more keywords
DTi2Vec: Drug–target interaction prediction using network embedding and ensemble learning
Sign Up to like & getrecommendations! Published in 2021 at "Journal of Cheminformatics"
DOI: 10.1186/s13321-021-00552-w
Abstract: Drug–target interaction (DTI) prediction is a crucial step in drug discovery and repositioning as it reduces experimental validation costs if done right. Thus, developing in-silico methods to predict potential DTI has become a competitive research… read more here.
Keywords: network; target interaction; drug; drug target ... See more keywords