AIMC Topic: Ligands

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Improved Prediction of Ligand-Protein Binding Affinities by Meta-modeling.

Journal of chemical information and modeling Nov 22, 2024
The accurate screening of candidate drug ligands against target proteins through computational approaches is of prime interest to drug development efforts. Such virtual screening depends in part on methods to predict the binding affinity between liga...
Ligands Molecular Docking Simulation Deep Learning Protein Binding Proteins Databases, Protein
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CPIScore: A Deep Learning Approach for Rapid Scoring and Interpretation of Protein-Ligand Binding Interactions.

Journal of chemical information and modeling Nov 19, 2024
Protein-ligand binding affinity prediction is a crucial and challenging task in the field of drug discovery. However, traditional simulation-based computational approaches are often prohibitively time-consuming, limiting their practical utility. In t...
Ligands Drug Discovery Small Molecule Libraries Deep Learning Protein Binding Proteins
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Deep learning pipeline for accelerating virtual screening in drug discovery.

Scientific reports Nov 16, 2024
In the race to combat ever-evolving diseases, the drug discovery process often faces the hurdles of high-cost and time-consuming procedures. To tackle these challenges and enhance the efficiency of identifying new therapeutic agents, we introduce Vir...
Molecular Docking Simulation Humans Drug Discovery Antiviral Agents Software Deep Learning Drug Evaluation, Preclinical Ligands
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Predicting Protein-Ligand Binding Affinity Using Fusion Model of Spatial-Temporal Graph Neural Network and 3D Structure-Based Complex Graph.

Interdisciplinary sciences, computational life sciences Nov 14, 2024
The investigation of molecular interactions between ligands and their target molecules is becoming more significant as protein structure data continues to develop. In this study, we introduce PLA-STGCNnet, a deep fusion spatial-temporal graph neural ...
Neural Networks, Computer Ligands Protein Conformation Graph Neural Networks Algorithms Protein Binding Proteins
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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...
Machine Learning Molecular Docking Simulation Protein Binding Molecular Dynamics Simulation Ligands Acetylcholinesterase Binding Sites Humans Cholinesterase Inhibitors Databases, Chemical
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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 ...
Ligands HIV Protease HIV Protease Inhibitors Hydrogen Bonding Models, Molecular Electrons HIV-1 Humans Machine Learning
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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...
Molecular Dynamics Simulation Ligands Peptides, Cyclic Machine Learning MicroRNAs Nucleic Acid Conformation RNA Precursors Ribonuclease III Humans
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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,...
Binding Sites Deep Learning Pharmaceutical Preparations Computational Biology Protein Binding Machine Learning Humans Ligands Proteins Algorithms Neural Networks, Computer Databases, Protein Drug Discovery
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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...
Humans Neural Networks, Computer Algorithms Proteins Drug Design Bayes Theorem Deep Learning Ligands
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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...
Ligands Protein Conformation Protein Binding Proteins Humans Neural Networks, Computer Machine Learning COVID-19 Algorithms SARS-CoV-2
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