AIMC Topic: Drug Discovery

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Artificial intelligence to deep learning: machine intelligence approach for drug discovery.

Molecular diversity Apr 12, 2021
Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design a...
Research Design Animals Drug Design Artificial Intelligence Machine Learning Algorithms Drug Development Support Vector Machine Models, Molecular Drug Discovery Deep Learning Big Data Humans Data Mining Quantitative Structure-Activity Relationship Chemistry Techniques, Synthetic
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Application of network link prediction in drug discovery.

BMC bioinformatics Apr 12, 2021
BACKGROUND: Technological and research advances have produced large volumes of biomedical data. When represented as a network (graph), these data become useful for modeling entities and interactions in biological and similar complex systems. In the f...
Machine Learning Algorithms Drug Discovery Drug Interactions
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Development of a graph convolutional neural network model for efficient prediction of protein-ligand binding affinities.

PloS one Apr 8, 2021
Prediction of protein-ligand interactions is a critical step during the initial phase of drug discovery. We propose a novel deep-learning-based prediction model based on a graph convolutional neural network, named GraphBAR, for protein-ligand binding...
Ligands Deep Learning Neural Networks, Computer Protein Binding Molecular Docking Simulation Proteins Drug Discovery
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Artificial intelligence in drug discovery: recent advances and future perspectives.

Expert opinion on drug discovery Apr 2, 2021
: Artificial intelligence (AI) has inspired computer-aided drug discovery. The widespread adoption of machine learning, in particular deep learning, in multiple scientific disciplines, and the advances in computing hardware and software, among other ...
Machine Learning Drug Design Drug Discovery Quantitative Structure-Activity Relationship Humans Artificial Intelligence
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Identification of novel inhibitors of Keap1/Nrf2 by a promising method combining protein-protein interaction-oriented library and machine learning.

Scientific reports Apr 1, 2021
Protein-protein interactions (PPIs) are prospective but challenging targets for drug discovery, because screening using traditional small-molecule libraries often fails to identify hits. Recently, we developed a PPI-oriented library comprising 12,593...
Binding Sites Small Molecule Libraries Protein Interaction Mapping Drug Discovery Ligands Protein Interaction Maps Molecular Conformation Protein Binding Kelch-Like ECH-Associated Protein 1 NF-E2-Related Factor 2 Structure-Activity Relationship Humans Molecular Structure Molecular Dynamics Simulation Machine Learning Molecular Docking Simulation
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Epigenetic Target Fishing with Accurate Machine Learning Models.

Journal of medicinal chemistry Mar 26, 2021
Epigenetic targets are of significant importance in drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represe...
Proof of Concept Study Histone Deacetylases Structure-Activity Relationship Molecular Structure Machine Learning Organic Chemicals Databases, Chemical Drug Discovery Epigenomics Transcription Factors
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EDock-ML: A web server for using ensemble docking with machine learning to aid drug discovery.

Protein science : a publication of the Protein Society Mar 25, 2021
EDock-ML is a web server that facilitates the use of ensemble docking with machine learning to help decide whether a compound is worthwhile to be considered further in a drug discovery process. Ensemble docking provides an economical way to account f...
Machine Learning Software Molecular Docking Simulation Drug Discovery Internet Databases, Chemical
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The challenges of generalizability in artificial intelligence for ADME/Tox endpoint and activity prediction.

Expert opinion on drug discovery Mar 19, 2021
INTRODUCTION: Artificial intelligence (AI) has seen a massive resurgence in recent years with wide successes in computer vision, natural language processing, and games. The similar creation of robust and accurate AI models for ADME/Tox endpoint and a...
Artificial Intelligence Humans Drug Design Drug Discovery
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Discovery of novel dual adenosine A1/A2A receptor antagonists using deep learning, pharmacophore modeling and molecular docking.

PLoS computational biology Mar 19, 2021
Adenosine receptors (ARs) have been demonstrated to be potential therapeutic targets against Parkinson's disease (PD). In the present study, we describe a multistage virtual screening approach that identifies dual adenosine A1 and A2A receptor antago...
Humans Adenosine A1 Receptor Antagonists Protein Binding Adenosine A2 Receptor Antagonists Molecular Docking Simulation Parkinson Disease Drug Discovery Receptor, Adenosine A2A Deep Learning
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Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations.

Nature biomedical engineering Mar 11, 2021
The de novo design of antimicrobial therapeutics involves the exploration of a vast chemical repertoire to find compounds with broad-spectrum potency and low toxicity. Here, we report an efficient computational method for the generation of antimicrob...
Acinetobacter baumannii Molecular Dynamics Simulation Amino Acid Sequence Pseudomonas aeruginosa Mice, Inbred BALB C Antimicrobial Cationic Peptides Structure-Activity Relationship Deep Learning Microbial Sensitivity Tests Mice Escherichia coli Animals Staphylococcus aureus Klebsiella pneumoniae Drug Design Klebsiella Infections Drug Discovery Anti-Bacterial Agents Drug Resistance, Bacterial Female
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