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Drug Repositioning

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Anti-Biofilm: Machine Learning Assisted Prediction of IC Activity of Chemicals Against Biofilms of Microbes Causing Antimicrobial Resistance and Implications in Drug Repurposing.

Journal of molecular biology
Biofilms are one of the leading causes of antibiotic resistance. It acts as a physical barrier against the human immune system and drugs. The use of anti-biofilm agents helps in tackling the menace of antibiotic resistance. The identification of effi...

DeepLPI: a novel deep learning-based model for protein-ligand interaction prediction for drug repurposing.

Scientific reports
The substantial cost of new drug research and development has consistently posed a huge burden for both pharmaceutical companies and patients. In order to lower the expenditure and development failure rate, repurposing existing and approved drugs by ...

Addressing Noise and Estimating Uncertainty in Biomedical Data through the Exploration of Chemical Space.

International journal of molecular sciences
Noise is a basic ingredient in data, since observed data are always contaminated by unwanted deviations, i.e., noise, which, in the case of overdetermined systems (with more data than model parameters), cause the corresponding linear system of equati...

Adera2.0: A Drug Repurposing Workflow for Neuroimmunological Investigations Using Neural Networks.

Molecules (Basel, Switzerland)
Drug repurposing in the context of neuroimmunological (NI) investigations is still in its primary stages. Drug repurposing is an important method that bypasses lengthy drug discovery procedures and focuses on discovering new usages for known medicati...

GCMM: graph convolution network based on multimodal attention mechanism for drug repurposing.

BMC bioinformatics
BACKGROUND: The main focus of in silico drug repurposing, which is a promising area for using artificial intelligence in drug discovery, is the prediction of drug-disease relationships. Although many computational models have been proposed recently, ...

Deep learning prediction of chemical-induced dose-dependent and context-specific multiplex phenotype responses and its application to personalized alzheimer's disease drug repurposing.

PLoS computational biology
Predictive modeling of drug-induced gene expressions is a powerful tool for phenotype-based compound screening and drug repurposing. State-of-the-art machine learning methods use a small number of fixed cell lines as a surrogate for predicting actual...

Does adding the drug-drug similarity to drug-target interaction prediction methods make a noticeable improvement in their efficiency?

BMC bioinformatics
Predicting drug-target interactions (DTIs) has become an important bioinformatics issue because it is one of the critical and preliminary stages of drug repositioning. Therefore, scientists are trying to develop more accurate computational methods fo...

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...

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...