AIMC Topic: Drug Repositioning

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DeepFusion: A deep learning based multi-scale feature fusion method for predicting drug-target interactions.

Methods (San Diego, Calif.)
Predicting drug-target interactions (DTIs) is essential for both drug discovery and drug repositioning. Recently, deep learning methods have achieved relatively significant performance in predicting DTIs. Generally, it needs a large amount of approve...

Defining clinical outcome pathways.

Drug discovery today
Here, we propose a broad concept of 'Clinical Outcome Pathways' (COPs), which are defined as a series of key molecular and cellular events that underlie therapeutic effects of drug molecules. We formalize COPs as a chain of the following events: mole...

Graph Convolutional Autoencoder and Generative Adversarial Network-Based Method for Predicting Drug-Target Interactions.

IEEE/ACM transactions on computational biology and bioinformatics
The computational prediction of novel drug-target interactions (DTIs) may effectively speed up the process of drug repositioning and reduce its costs. Most previous methods integrated multiple kinds of connections about drugs and targets by construct...

DOTA: Deep Learning Optimal Transport Approach to Advance Drug Repositioning for Alzheimer's Disease.

Biomolecules
Alzheimer's disease (AD) is the leading cause of age-related dementia, affecting over 5 million people in the United States and incurring a substantial global healthcare cost. Unfortunately, current treatments are only palliative and do not cure AD. ...

Ontology-based identification and prioritization of candidate drugs for epilepsy from literature.

Journal of biomedical semantics
BACKGROUND: Drug repurposing can improve the return of investment as it finds new uses for existing drugs. Literature-based analyses exploit factual knowledge on drugs and diseases, e.g. from databases, and combine it with information from scholarly ...

Machine learning and deep learning in data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry.

Future medicinal chemistry
Predicting novel small molecule bioactivities for the target deconvolution, hit-to-lead optimization in drug discovery research, requires molecular representation. Previous reports have demonstrated that machine learning (ML) and deep learning (DL) h...

Drug-Target Interaction Prediction: End-to-End Deep Learning Approach.

IEEE/ACM transactions on computational biology and bioinformatics
The discovery of potential Drug-Target Interactions (DTIs) is a determining step in the drug discovery and repositioning process, as the effectiveness of the currently available antibiotic treatment is declining. Although putting efforts on the tradi...

Drug repurposing for COVID-19 using graph neural network and harmonizing multiple evidence.

Scientific reports
Since the 2019 novel coronavirus disease (COVID-19) outbreak in 2019 and the pandemic continues for more than one year, a vast amount of drug research has been conducted and few of them got FDA approval. Our objective is to prioritize repurposable dr...

A unified drug-target interaction prediction framework based on knowledge graph and recommendation system.

Nature communications
Prediction of drug-target interactions (DTI) plays a vital role in drug development in various areas, such as virtual screening, drug repurposing and identification of potential drug side effects. Despite extensive efforts have been invested in perfe...

Combined deep learning and molecular docking simulations approach identifies potentially effective FDA approved drugs for repurposing against SARS-CoV-2.

Computers in biology and medicine
The ongoing pandemic of Coronavirus Disease 2019 (COVID-19) has posed a serious threat to global public health. Drug repurposing is a time-efficient approach to finding effective drugs against SARS-CoV-2 in this emergency. Here, we present a robust e...