AIMC Topic: Drug Discovery

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A novel artificial intelligence protocol to investigate potential leads for diabetes mellitus.

Molecular diversity Mar 9, 2021
Dipeptidyl peptidase-4 (DPP4) is highly participated in regulating diabetes mellitus (DM), and inhibitors of DPP4 may act as potential DM drugs. Therefore, we performed a novel artificial intelligence (AI) protocol to screen and validate the potentia...
Algorithms Molecular Docking Simulation Drug Design Neural Networks, Computer Artificial Intelligence Machine Learning Molecular Structure Workflow Molecular Dynamics Simulation Drug Discovery Protein Binding Hypoglycemic Agents Humans Dipeptidyl-Peptidase IV Inhibitors Quantitative Structure-Activity Relationship Hydrogen Bonding Binding Sites
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Expanding the drug discovery space with predicted metabolite-target interactions.

Communications biology Mar 5, 2021
Metabolites produced in the human gut are known modulators of host immunity. However, large-scale identification of metabolite-host receptor interactions remains a daunting challenge. Here, we employed computational approaches to identify 983 potenti...
Gastrointestinal Microbiome Cells, Cultured Metabolomics Metabolome Databases, Factual Inflammatory Bowel Diseases Drug Discovery Protein Interaction Maps Gene Expression Profiling Bacteria Ligands Humans Transcriptome Anti-Inflammatory Agents Signal Transduction Molecular Targeted Therapy Gastrointestinal Agents Machine Learning Host-Pathogen Interactions Data Mining
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Trends in application of advancing computational approaches in GPCR ligand discovery.

Experimental biology and medicine (Maywood, N.J.) Feb 27, 2021
G protein-coupled receptors (GPCRs) comprise the most important superfamily of protein targets in current ligand discovery and drug development. GPCRs are integral membrane proteins that play key roles in various cellular signaling processes. Therefo...
Drug Discovery Receptors, G-Protein-Coupled Humans Ligands Machine Learning Drug Design
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Coloring Molecules with Explainable Artificial Intelligence for Preclinical Relevance Assessment.

Journal of chemical information and modeling Feb 25, 2021
Graph neural networks are able to solve certain drug discovery tasks such as molecular property prediction and molecule generation. However, these models are considered "black-box" and "hard-to-debug". This study aimed to improve modeling transparen...
Neural Networks, Computer Artificial Intelligence Drug Discovery Ligands
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Practical constraints with machine learning in drug discovery.

Expert opinion on drug discovery Feb 19, 2021
Humans Machine Learning Drug Discovery
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Prioritizing Pain-Associated Targets with Machine Learning.

Biochemistry Feb 19, 2021
While hundreds of genes have been associated with pain, much of the molecular mechanisms of pain remain unknown. As a result, current analgesics are limited to few clinically validated targets. Here, we trained a machine learning (ML) ensemble model ...
Humans Machine Learning Analgesics Models, Biological Pain Drug Delivery Systems Drug Design Drug Discovery
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Graph neural networks for automated de novo drug design.

Drug discovery today Feb 17, 2021
The goal of de novo drug design is to create novel chemical entities with desired biological activities and pharmacokinetics (PK) properties. Over recent years, with the development of artificial intelligence (AI) technologies, data-driven methods ha...
Drug Discovery Artificial Intelligence Technology Drug Design Humans Neural Networks, Computer
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TranSynergy: Mechanism-driven interpretable deep neural network for the synergistic prediction and pathway deconvolution of drug combinations.

PLoS computational biology Feb 12, 2021
Drug combinations have demonstrated great potential in cancer treatments. They alleviate drug resistance and improve therapeutic efficacy. The fast-growing number of anti-cancer drugs has caused the experimental investigation of all drug combinations...
Computer Simulation Precision Medicine Drug Discovery Antineoplastic Agents Deep Learning Computational Biology Humans Neural Networks, Computer Algorithms Drug Combinations Drug Evaluation, Preclinical Drug Synergism
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Predicting Potential SARS-COV-2 Drugs-In Depth Drug Database Screening Using Deep Neural Network Framework SSnet, Classical Virtual Screening and Docking.

International journal of molecular sciences Feb 4, 2021
Severe Acute Respiratory Syndrome Corona Virus 2 has altered life on a global scale. A concerted effort from research labs around the world resulted in the identification of potential pharmaceutical treatments for CoVID-19 using existing drugs, as we...
COVID-19 Drug Treatment Angiotensin-Converting Enzyme 2 Databases, Pharmaceutical Spike Glycoprotein, Coronavirus Drug Repositioning Drug Evaluation, Preclinical COVID-19 Algorithms SARS-CoV-2 Molecular Docking Simulation Drug Discovery Antiviral Agents Humans Neural Networks, Computer Machine Learning
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Generative chemistry: drug discovery with deep learning generative models.

Journal of molecular modeling Feb 4, 2021
The de novo design of molecular structures using deep learning generative models introduces an encouraging solution to drug discovery in the face of the continuously increased cost of new drug development. From the generation of original texts, image...
Neural Networks, Computer Artificial Intelligence Drug Discovery Deep Learning Models, Chemical
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