Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions
Neurological disorders significantly outnumber diseases in other therapeutic areas.
However, developing drugs for central nervous system (CNS) disorders remains the most …
However, developing drugs for central nervous system (CNS) disorders remains the most …
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 …
Identifying drug–target interactions based on graph convolutional network and deep neural network
Identification of new drug–target interactions (DTIs) is an important but a time-consuming
and costly step in drug discovery. In recent years, to mitigate these drawbacks, researchers …
and costly step in drug discovery. In recent years, to mitigate these drawbacks, researchers …
[HTML][HTML] NeoDTI: neural integration of neighbor information from a heterogeneous network for discovering new drug–target interactions
Results Inspired by recent advance of information passing and aggregation techniques that
generalize the convolution neural networks to mine large-scale graph data and greatly …
generalize the convolution neural networks to mine large-scale graph data and greatly …
Computational prediction of drug–target interactions using chemogenomic approaches: an empirical survey
Computational prediction of drug–target interactions (DTIs) has become an essential task in
the drug discovery process. It narrows down the search space for interactions by suggesting …
the drug discovery process. It narrows down the search space for interactions by suggesting …
DDR: efficient computational method to predict drug–target interactions using graph mining and machine learning approaches
Motivation Finding computationally drug–target interactions (DTIs) is a convenient strategy
to identify new DTIs at low cost with reasonable accuracy. However, the current DTI …
to identify new DTIs at low cost with reasonable accuracy. However, the current DTI …
BioSeq-Diabolo: biological sequence similarity analysis using Diabolo
H Li, B Liu - PLoS computational biology, 2023 - journals.plos.org
As the key for biological sequence structure and function prediction, disease diagnosis and
treatment, biological sequence similarity analysis has attracted more and more attentions …
treatment, biological sequence similarity analysis has attracted more and more attentions …
Predicting drug-target interactions using Lasso with random forest based on evolutionary information and chemical structure
H Shi, S Liu, J Chen, X Li, Q Ma, B Yu - Genomics, 2019 - Elsevier
The identification of drug-target interactions has great significance for pharmaceutical
scientific research. Since traditional experimental methods identifying drug-target …
scientific research. Since traditional experimental methods identifying drug-target …
DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features
Drug–target interactions (DTIs) play a crucial role in target-based drug discovery and
development. Computational prediction of DTIs can effectively complement experimental …
development. Computational prediction of DTIs can effectively complement experimental …
GOLabeler: improving sequence-based large-scale protein function prediction by learning to rank
Abstract Motivation Gene Ontology (GO) has been widely used to annotate functions of
proteins and understand their biological roles. Currently only< 1% of> 70 million proteins in …
proteins and understand their biological roles. Currently only< 1% of> 70 million proteins in …