AIMC Topic: Ligands

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Machine learning and molecular simulation ascertain antimicrobial peptide against Klebsiella pneumoniae from public database.

Computational biology and chemistry Dec 5, 2022
Antimicrobial peptides (AMPs) are short peptides with a broad spectrum of antimicrobial activity. They play a key role in the host innate immunity of many organisms. The growing threat of microorganisms resistant to antimicrobial agents and the lack ...
Anti-Bacterial Agents Molecular Docking Simulation Machine Learning Ligands Antimicrobial Peptides Molecular Dynamics Simulation Klebsiella pneumoniae
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Reliable prediction of cannabinoid receptor 2 ligand by machine learning based on combined fingerprints.

Computers in biology and medicine Nov 30, 2022
Cannabinoid receptors, as part of the family of the G protein-coupled receptors (GPCRs), are involved in various physiological functions. Its subtype cannabinoid receptor subtype 2 (CB2), mainly distributed in the periphery, is a crucial therapeutic ...
Receptors, Cannabinoid Machine Learning Ligands Random Forest
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Multi-source transfer learning with Graph Neural Network for excellent modelling the bioactivities of ligands targeting orphan G protein-coupled receptors.

Mathematical biosciences and engineering : MBE Nov 25, 2022
G protein-coupled receptors (GPCRs) have been the targets for more than 40% of the currently approved drugs. Although neural networks can effectively improve the accuracy of prediction with the biological activity, the result is undesirable in the li...
Signal Transduction Receptors, G-Protein-Coupled Machine Learning Ligands Neural Networks, Computer
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AlphaFill: enriching AlphaFold models with ligands and cofactors.

Nature methods Nov 24, 2022
Artificial intelligence-based protein structure prediction approaches have had a transformative effect on biomolecular sciences. The predicted protein models in the AlphaFold protein structure database, however, all lack coordinates for small molecul...
Protein Conformation Ions Ligands Proteins Zinc Artificial Intelligence
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Few-Shot Learning for Low-Data Drug Discovery.

Journal of chemical information and modeling Nov 21, 2022
The discovery of new hits through ligand-based virtual screening in drug discovery is essentially a low-data problem, as data acquisition is both difficult and expensive. The requirement for large amounts of training data hinders the application of c...
Neural Networks, Computer Drug Discovery Ligands Machine Learning
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Hybrid-Enhanced Siamese Similarity Models in Ligand-Based Virtual Screen.

Biomolecules Nov 20, 2022
Information technology has become an integral aspect of the drug development process. The virtual screening process (VS) is a computational technique for screening chemical compounds in a reasonable amount of time and cost. The similarity search is o...
Mental Recall Ligands Databases, Factual Neural Networks, Computer
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Deffini: A family-specific deep neural network model for structure-based virtual screening.

Computers in biology and medicine Nov 17, 2022
Deep learning-based virtual screening methods have been shown to significantly improve the accuracy of traditional docking-based virtual screening methods. In this paper, we developed Deffini, a structure-based virtual screening neural network model....
Proteins Ligands Neural Networks, Computer
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A New Hybrid Neural Network Deep Learning Method for Protein-Ligand Binding Affinity Prediction and De Novo Drug Design.

International journal of molecular sciences Nov 11, 2022
Accurately predicting ligand binding affinity in a virtual screening campaign is still challenging. Here, we developed hybrid neural network (HNN) machine deep learning methods, HNN-denovo and HNN-affinity, by combining the 3D-CNN (convolutional neur...
Ligands Drug Design Neural Networks, Computer Proteins Deep Learning Molecular Docking Simulation
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MILCDock: Machine Learning Enhanced Consensus Docking for Virtual Screening in Drug Discovery.

Journal of chemical information and modeling Nov 7, 2022
Molecular docking tools are regularly used to computationally identify new molecules in virtual screening for drug discovery. However, docking tools suffer from inaccurate scoring functions with widely varying performance on different proteins. To en...
Consensus Ligands Protein Binding Molecular Docking Simulation Drug Discovery Machine Learning
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Prediction of drug-target interactions through multi-task learning.

Scientific reports Oct 31, 2022
Identifying the binding between the target proteins and molecules is essential in drug discovery. The multi-task learning method has been introduced to facilitate knowledge sharing among tasks when the amount of information for each task is small. Ho...
Proteins Machine Learning Ligands Drug Discovery Learning
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