AIMC Topic: Ligands

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Estimating AChE inhibitors from MCE database by machine learning and atomistic calculations.

Journal of molecular graphics & modelling Nov 14, 2024
Acetylcholinesterase (AChE) is one of the most successful targets for the treatment of Alzheimer's disease (AD). Inhibition of AChE can result in preventing AD. In this context, the machine-learning (ML) model, molecular docking, and molecular dynami...
Acetylcholinesterase Cholinesterase Inhibitors Databases, Chemical Humans Binding Sites Protein Binding Machine Learning Molecular Dynamics Simulation Ligands Molecular Docking Simulation
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Machine learning-based prediction of bioactivity in HIV-1 protease: insights from electron density analysis.

Future medicinal chemistry Nov 12, 2024
To develop a model for predicting the biological activity of compounds targeting the HIV-1 protease and to establish factors influencing enzyme inhibition. Machine learning models were built based on a combination of Richard Bader's theory of Atoms ...
Humans Machine Learning Hydrogen Bonding Ligands Models, Molecular HIV Protease Electrons HIV Protease Inhibitors HIV-1
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Investigating Ligand-Mediated Conformational Dynamics of Pre-miR21: A Machine-Learning-Aided Enhanced Sampling Study.

Journal of chemical information and modeling Nov 11, 2024
MicroRNAs (miRs) are short, noncoding RNA strands that regulate the activity of mRNAs by affecting the repression of protein translation, and their dysregulation has been implicated in several pathologies. miR21 in particular has been implicated in t...
RNA Precursors Ribonuclease III Peptides, Cyclic Nucleic Acid Conformation Humans Machine Learning MicroRNAs Molecular Dynamics Simulation Ligands
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Improving drug-target interaction prediction through dual-modality fusion with InteractNet.

Journal of bioinformatics and computational biology Nov 11, 2024
In the drug discovery process, accurate prediction of drug-target interactions is crucial to accelerate the development of new drugs. However, existing methods still face many challenges in dealing with complex biomolecular interactions. To this end,...
Ligands Binding Sites Proteins Machine Learning Deep Learning Databases, Protein Algorithms Protein Binding Humans Pharmaceutical Preparations Drug Discovery Computational Biology Neural Networks, Computer
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A deep learning approach for rational ligand generation with toxicity control via reactive building blocks.

Nature computational science Nov 8, 2024
Deep generative models are gaining attention in the field of de novo drug design. However, the rational design of ligand molecules for novel targets remains challenging, particularly in controlling the properties of the generated molecules. Here, ins...
Ligands Neural Networks, Computer Algorithms Drug Design Deep Learning Proteins Bayes Theorem Humans
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Interaction-Based Inductive Bias in Graph Neural Networks: Enhancing Protein-Ligand Binding Affinity Predictions From 3D Structures.

IEEE transactions on pattern analysis and machine intelligence Nov 6, 2024
Inductive bias in machine learning (ML) is the set of assumptions describing how a model makes predictions. Different ML-based methods for protein-ligand binding affinity (PLA) prediction have different inductive biases, leading to different levels o...
Protein Conformation Protein Binding Proteins Ligands Humans Neural Networks, Computer Machine Learning COVID-19 Algorithms SARS-CoV-2
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GeoNet enables the accurate prediction of protein-ligand binding sites through interpretable geometric deep learning.

Structure (London, England : 1993) Nov 1, 2024
The identification of protein binding residues is essential for understanding their functions in vivo. However, it remains a computational challenge to accurately identify binding sites due to the lack of known residue binding patterns. Local residue...
Computational Biology Proteins DNA Deep Learning Protein Binding Binding Sites Models, Molecular RNA Ligands
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Challenge for Deep Learning: Protein Structure Prediction of Ligand-Induced Conformational Changes at Allosteric and Orthosteric Sites.

Journal of chemical information and modeling Nov 1, 2024
In the realm of biomedical research, understanding the intricate structure of proteins is crucial, as these structures determine how proteins function within our bodies and interact with potential drugs. Traditionally, methods like X-ray crystallogra...
Deep Learning Proteins Ligands Databases, Protein Protein Conformation Models, Molecular Allosteric Site
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AlzyFinder: A Machine-Learning-Driven Platform for Ligand-Based Virtual Screening and Network Pharmacology.

Journal of chemical information and modeling Oct 31, 2024
Alzheimer's disease (AD), a prevalent neurodegenerative disorder, presents significant challenges in drug development due to its multifactorial nature and complex pathophysiology. The AlzyFinder Platform, introduced in this study, addresses these cha...
Humans Alzheimer Disease Machine Learning Ligands Network Pharmacology Drug Discovery User-Computer Interface Drug Evaluation, Preclinical
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Molecular tweaking by generative cheminformatics and ligand-protein structures for rational drug discovery.

Bioorganic chemistry Oct 28, 2024
The purpose of this review is two-fold: (1) to summarize artificial intelligence and machine learning approaches and document the role of ligand-protein structures in directing drug discovery; (2) to present examples of drugs from the recent literatu...
Humans Artificial Intelligence Ligands Machine Learning Cheminformatics Drug Discovery Proteins Molecular Structure
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