AIMC Topic: Ligands

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Structure-Aware Graph Attention Diffusion Network for Protein-Ligand Binding Affinity Prediction.

IEEE transactions on neural networks and learning systems Dec 2, 2024
Accurate prediction of protein-ligand binding affinities can significantly advance the development of drug discovery. Several graph neural network (GNN)-based methods learn representations of protein-ligand complexes via modeling intermolecule intera...
Databases, Protein Ligands Drug Discovery Humans Protein Binding Neural Networks, Computer Proteins Algorithms
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Generative adversarial network (GAN) model-based design of potent SARS-CoV-2 M inhibitors using the electron density of ligands and 3D binding pockets: insights from molecular docking, dynamics simulation, and MM-GBSA analysis.

Molecular diversity Nov 30, 2024
Deep learning-based generative adversarial network (GAN) frameworks have recently been developed to expedite the drug discovery process. These models generate novel molecules from scratch and validate them through molecular docking simulation to iden...
Humans COVID-19 Coronavirus 3C Proteases Generative Adversarial Networks Drug Design SARS-CoV-2 COVID-19 Drug Treatment Deep Learning Molecular Docking Simulation Binding Sites Antiviral Agents Protein Binding Protease Inhibitors Molecular Dynamics Simulation Ligands
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SurfDock is a surface-informed diffusion generative model for reliable and accurate protein-ligand complex prediction.

Nature methods Nov 27, 2024
Accurately predicting protein-ligand interactions is crucial for understanding cellular processes. We introduce SurfDock, a deep-learning method that addresses this challenge by integrating protein sequence, three-dimensional structural graphs and su...
Protein Binding Protein Conformation Algorithms Humans Proteins Molecular Docking Simulation Software Ligands Deep Learning
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Triple-Action Therapy: Combining Machine Learning, Docking, and Dynamics to Combat BRCA1-Mutated Breast Cancer.

Molecular biotechnology Nov 26, 2024
Breast cancer dominates women's mortality, and among other factors, mutations in the BRCA1 gene are significant risk factors. Several approaches are followed to treat the BRCA1 affected cancer patients. However, specific BRCA1 inhibitors are not avai...
Machine Learning Female Breast Neoplasms Humans Molecular Dynamics Simulation Ligands BRCA1 Protein Molecular Docking Simulation Antineoplastic Agents Mutation
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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...
Databases, Protein Ligands Protein Binding Proteins Molecular Docking Simulation Deep Learning
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MERIT: Accurate Prediction of Multi Ligand-binding Residues with Hybrid Deep Transformer Network, Evolutionary Couplings and Transfer Learning.

Journal of molecular biology Nov 20, 2024
Multi-ligand binding residues (MLBRs) are amino acids in protein sequences that interact with multiple different ligands that include proteins, peptides, nucleic acids, and a variety of small molecules. MLBRs are implicated in a number of cellular fu...
Deep Learning Protein Binding Computational Biology Machine Learning Software Binding Sites Proteins Databases, Protein Ligands Humans Neural Networks, Computer
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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...
Drug Discovery Ligands Deep Learning Small Molecule Libraries 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...
Humans Software Molecular Docking Simulation Antiviral Agents Drug Discovery 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 ...
Proteins Ligands Protein Conformation Graph Neural Networks Neural Networks, Computer Algorithms Protein Binding
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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 Binding Sites Protein Binding Humans Machine Learning Molecular Dynamics Simulation Ligands Molecular Docking Simulation
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