AIMC Topic: Drug Repositioning
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DOTA: Deep Learning Optimal Transport Approach to Advance Drug Repositioning for Alzheimer's Disease.
Biomolecules
Jan 24, 2022
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
Jan 24, 2022
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
Dec 23, 2021
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
Dec 8, 2021
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
Nov 30, 2021
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
Nov 22, 2021
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
Nov 20, 2021
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...
Comparative analysis of network-based approaches and machine learning algorithms for predicting drug-target interactions.
Methods (San Diego, Calif.)
Nov 1, 2021
Computational prediction of drug-target interactions (DTIs) is of particular importance in the process of drug repositioning because of its efficiency in selecting potential candidates for DTIs. A variety of computational methods for predicting DTIs ...
Deep learning in target prediction and drug repositioning: Recent advances and challenges.
Drug discovery today
Oct 27, 2021
Drug repositioning is an attractive strategy for discovering new therapeutic uses for approved or investigational drugs, with potentially shorter development timelines and lower development costs. Various computational methods have been used in drug ...
New Insights Into Drug Repurposing for COVID-19 Using Deep Learning.
IEEE transactions on neural networks and learning systems
Oct 27, 2021
The coronavirus disease 2019 (COVID-19) has continued to spread worldwide since late 2019. To expedite the process of providing treatment to those who have contracted the disease and to ensure the accessibility of effective drugs, numerous strategies...
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