Machine learning approaches and databases for prediction of drug–target interaction: a survey paper
The task of predicting the interactions between drugs and targets plays a key role in the
process of drug discovery. There is a need to develop novel and efficient prediction …
process of drug discovery. There is a need to develop novel and efficient prediction …
Drug–target interaction prediction: databases, web servers and computational models
Identification of drug–target interactions is an important process in drug discovery. Although
high-throughput screening and other biological assays are becoming available …
high-throughput screening and other biological assays are becoming available …
GraphDTA: predicting drug–target binding affinity with graph neural networks
The development of new drugs is costly, time consuming and often accompanied with safety
issues. Drug repurposing can avoid the expensive and lengthy process of drug development …
issues. Drug repurposing can avoid the expensive and lengthy process of drug development …
Systematic integration of biomedical knowledge prioritizes drugs for repurposing
The ability to computationally predict whether a compound treats a disease would improve
the economy and success rate of drug approval. This study describes Project Rephetio to …
the economy and success rate of drug approval. This study describes Project Rephetio to …
Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review
Despite considerable progress in genome-and proteome-based high-throughput screening
methods and in rational drug design, the increase in approved drugs in the past decade did …
methods and in rational drug design, the increase in approved drugs in the past decade did …
Similarity-based machine learning methods for predicting drug–target interactions: a brief review
Computationally predicting drug–target interactions is useful to select possible drug (or
target) candidates for further biochemical verification. We focus on machine learning-based …
target) candidates for further biochemical verification. We focus on machine learning-based …
Collaborative matrix factorization with multiple similarities for predicting drug-target interactions
We address the problem of predicting new drug-target interactions from three inputs: known
interactions, similarities over drugs and those over targets. This setting has been considered …
interactions, similarities over drugs and those over targets. This setting has been considered …
[HTML][HTML] Drug resistance in nontuberculous mycobacteria: mechanisms and models
Simple Summary Recently, there has been a considerable rise in infections caused by
nontuberculous mycobacteria (NTM). These mycobacteria, which comprise a large and …
nontuberculous mycobacteria (NTM). These mycobacteria, which comprise a large and …
A computational-based method for predicting drug–target interactions by using stacked autoencoder deep neural network
Identifying the interaction between drugs and target proteins is an important area of drug
research, which provides a broad prospect for low-risk and faster drug development …
research, which provides a broad prospect for low-risk and faster drug development …
[HTML][HTML] Transcriptional data: a new gateway to drug repositioning?
Recent advances in computational biology suggest that any perturbation to the
transcriptional programme of the cell can be summarised by a proper 'signature': a set of …
transcriptional programme of the cell can be summarised by a proper 'signature': a set of …