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Drug Development

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Data considerations for predictive modeling applied to the discovery of bioactive natural products.

Drug discovery today
Natural products (NPs) constitute a large reserve of bioactive compounds useful for drug development. Recent advances in high-throughput technologies facilitate functional analysis of therapeutic effects and NP-based drug discovery. However, the larg...

DeepNC: a framework for drug-target interaction prediction with graph neural networks.

PeerJ
The exploration of drug-target interactions (DTI) is an essential stage in the drug development pipeline. Thanks to the assistance of computational models, notably in the deep learning approach, scientists have been able to shorten the time spent on ...

Learning Multi-Scale Heterogeneous Representations and Global Topology for Drug-Target Interaction Prediction.

IEEE journal of biomedical and health informatics
Identification of interactions between drugs and target proteins plays a critical role not only in drug discovery but also in drug repositioning. Deep integration of inter-connections and intra-similarities between heterogeneous multi-source data abo...

HGDTI: predicting drug-target interaction by using information aggregation based on heterogeneous graph neural network.

BMC bioinformatics
BACKGROUND: In research on new drug discovery, the traditional wet experiment has a long period. Predicting drug-target interaction (DTI) in silico can greatly narrow the scope of search of candidate medications. Excellent algorithm model may be more...

GEFA: Early Fusion Approach in Drug-Target Affinity Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Predicting the interaction between a compound and a target is crucial for rapid drug repurposing. Deep learning has been successfully applied in drug-target affinity (DTA)problem. However, previous deep learning-based methods ignore modeling the dire...

Integration of Neighbor Topologies Based on Meta-Paths and Node Attributes for Predicting Drug-Related Diseases.

International journal of molecular sciences
Identifying new disease indications for existing drugs can help facilitate drug development and reduce development cost. The previous drug-disease association prediction methods focused on data about drugs and diseases from multiple sources. However,...

Affinity2Vec: drug-target binding affinity prediction through representation learning, graph mining, and machine learning.

Scientific reports
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 p...

CNN-DDI: a learning-based method for predicting drug-drug interactions using convolution neural networks.

BMC bioinformatics
BACKGROUND: Drug-drug interactions (DDIs) are the reactions between drugs. They are compartmentalized into three types: synergistic, antagonistic and no reaction. As a rapidly developing technology, predicting DDIs-associated events is getting more a...

How paediatric drug development and use could benefit from OMICs: A c4c expert group white paper.

British journal of clinical pharmacology
The safety and efficacy of pharmacotherapy in children, particularly preterms, neonates and infants, is limited by a paucity of good-quality data from prospective clinical drug trials. A specific challenge is the establishment of valid biomarkers. OM...

Graph neural network approaches for drug-target interactions.

Current opinion in structural biology
Developing new drugs remains prohibitively expensive, time-consuming, and often involves safety issues. Accurate prediction of drug-target interactions (DTIs) can guide the drug discovery process and thus facilitate drug development. Non-Euclidian da...