AIMC Topic: Drug Development

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

BMC bioinformatics Apr 12, 2022
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...
Neural Networks, Computer Drug Development Proteins Drug Interactions Algorithms Computational Biology
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GEFA: Early Fusion Approach in Drug-Target Affinity Prediction.

IEEE/ACM transactions on computational biology and bioinformatics Apr 1, 2022
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...
Drug Repositioning Amino Acid Sequence Drug Development Proteins Neural Networks, Computer
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Integration of Neighbor Topologies Based on Meta-Paths and Node Attributes for Predicting Drug-Related Diseases.

International journal of molecular sciences Mar 31, 2022
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,...
Computational Biology Drug Development Mental Recall Neural Networks, Computer Algorithms
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Affinity2Vec: drug-target binding affinity prediction through representation learning, graph mining, and machine learning.

Scientific reports Mar 19, 2022
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...
Drug Repositioning Machine Learning Drug Development
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CNN-DDI: a learning-based method for predicting drug-drug interactions using convolution neural networks.

BMC bioinformatics Mar 7, 2022
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...
Algorithms Neural Networks, Computer Research Design Drug Interactions Drug Development
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How paediatric drug development and use could benefit from OMICs: A c4c expert group white paper.

British journal of clinical pharmacology Feb 9, 2022
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...
Metabolomics Biological Specimen Banks Pediatrics Child Drug Development Humans Prospective Studies Biomarkers Artificial Intelligence
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Graph neural network approaches for drug-target interactions.

Current opinion in structural biology Jan 21, 2022
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...
Molecular Structure Drug Interactions Neural Networks, Computer Drug Discovery Drug Development
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Advancing pharmacy and healthcare with virtual digital technologies.

Advanced drug delivery reviews Jan 5, 2022
Digitalisation of the healthcare sector promises to revolutionise patient healthcare globally. From the different technologies, virtual tools including artificial intelligence, blockchain, virtual, and augmented reality, to name but a few, are provid...
Drug Development Remote Sensing Technology Digital Technology Drug Industry United States Food and Drug Administration Machine Learning Humans Mobile Applications Research Design Virtual Reality Biomedical Technology Artificial Intelligence Drug Discovery Clinical Trials as Topic Time Factors United States Health Information Exchange Health Care Sector
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Machine Learning and Artificial Intelligence in Pharmaceutical Research and Development: a Review.

The AAPS journal Jan 4, 2022
Over the past decade, artificial intelligence (AI) and machine learning (ML) have become the breakthrough technology most anticipated to have a transformative effect on pharmaceutical research and development (R&D). This is partially driven by revolu...
Pharmaceutical Research Artificial Intelligence Machine Learning Diffusion of Innovation Research Design Computational Biology Clinical Trials as Topic Animals Drug Development Forecasting Humans
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Data Centric Molecular Analysis and Evaluation of Hepatocellular Carcinoma Therapeutics Using Machine Intelligence-Based Tools.

Journal of gastrointestinal cancer Dec 15, 2021
PURPOSE: Computational approaches have been used at different stages of drug development with the purpose of decreasing the time and cost of conventional experimental procedures. Lately, techniques mainly developed and applied in the field of artific...
Molecular Targeted Therapy Antineoplastic Agents Drug Development Humans Liver Neoplasms Artificial Intelligence Carcinoma, Hepatocellular Computational Biology
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