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Ligands

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Target-Specific Drug Design Method Combining Deep Learning and Water Pharmacophore.

Journal of chemical information and modeling Nov 30, 2020
Following identification of a target protein, hit identification, which finds small organic molecules that bind to the target, is an important first step of a structure-based drug design project. In this study, we demonstrate a target-specific drug d...
Ligands Molecular Docking Simulation Drug Design Water Proteins Deep Learning Algorithms
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Assessing hERG1 Blockade from Bayesian Machine-Learning-Optimized Site Identification by Ligand Competitive Saturation Simulations.

Journal of chemical information and modeling Nov 16, 2020
Drug-induced cardiotoxicity is a potentially lethal and yet one of the most common side effects with the drugs in clinical use. Most of the drug-induced cardiotoxicity is associated with an off-target pharmacological blockade of K currents carried ou...
Machine Learning Molecular Docking Simulation Models, Molecular Humans Ether-A-Go-Go Potassium Channels Bayes Theorem Ligands
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Machine learning-based prediction of enzyme substrate scope: Application to bacterial nitrilases.

Proteins Nov 10, 2020
Predicting the range of substrates accepted by an enzyme from its amino acid sequence is challenging. Although sequence- and structure-based annotation approaches are often accurate for predicting broad categories of substrate specificity, they gener...
Molecular Docking Simulation Protein Binding Bacterial Proteins Machine Learning Catalytic Domain Ligands Chemical Phenomena Nitriles Aminohydrolases
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AK-Score: Accurate Protein-Ligand Binding Affinity Prediction Using an Ensemble of 3D-Convolutional Neural Networks.

International journal of molecular sciences Nov 10, 2020
Accurate prediction of the binding affinity of a protein-ligand complex is essential for efficient and successful rational drug design. Therefore, many binding affinity prediction methods have been developed. In recent years, since deep learning tech...
Humans Ligands Neural Networks, Computer User-Computer Interface Drug Design Protein Binding Drug Discovery Molecular Docking Simulation Databases, Protein Deep Learning Proteins Computer-Aided Design Molecular Dynamics Simulation
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Guiding Conventional Protein-Ligand Docking Software with Convolutional Neural Networks.

Journal of chemical information and modeling Oct 14, 2020
The high-performance computational techniques have brought significant benefits for drug discovery efforts in recent decades. One of the most challenging problems in drug discovery is the protein-ligand binding pose prediction. To predict the most st...
Proteins Software Ligands Neural Networks, Computer Molecular Docking Simulation Protein Binding
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Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications.

Journal of computer-aided molecular design Oct 9, 2020
Atomistic simulations have become an invaluable tool for industrial applications ranging from the optimization of protein-ligand interactions for drug discovery to the design of new materials for energy applications. Here we review recent advances in...
Quantum Theory Ligands Models, Molecular Pharmaceutical Preparations Drug Design Drug Discovery Machine Learning
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Comparison of Scaling Methods to Obtain Calibrated Probabilities of Activity for Protein-Ligand Predictions.

Journal of chemical information and modeling Sep 21, 2020
In the context of bioactivity prediction, the question of how to calibrate a score produced by a machine learning method into a probability of binding to a protein target is not yet satisfactorily addressed. In this study, we compared the performance...
Probability Support Vector Machine Machine Learning Bayes Theorem Ligands
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Emulating Docking Results Using a Deep Neural Network: A New Perspective for Virtual Screening.

Journal of chemical information and modeling Sep 16, 2020
Docking is one of the most important steps in virtual screening pipelines, and it is an established method for examining potential interactions between ligands and receptors. However, this method is computationally expensive, and it is often among th...
Neural Networks, Computer Molecular Docking Simulation Ligands Protein Binding Receptors, G-Protein-Coupled Software Retrospective Studies
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Three-Dimensional Convolutional Neural Networks and a Cross-Docked Data Set for Structure-Based Drug Design.

Journal of chemical information and modeling Sep 10, 2020
One of the main challenges in drug discovery is predicting protein-ligand binding affinity. Recently, machine learning approaches have made substantial progress on this task. However, current methods of model evaluation are overly optimistic in measu...
Neural Networks, Computer Protein Binding Databases, Protein Drug Design Ligands
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Prediction of Specific TCR-Peptide Binding From Large Dictionaries of TCR-Peptide Pairs.

Frontiers in immunology Aug 25, 2020
Current sequencing methods allow for detailed samples of T cell receptors (TCR) repertoires. To determine from a repertoire whether its host had been exposed to a target, computational tools that predict TCR-epitope binding are required. Currents too...
Binding Sites Protein Interaction Domains and Motifs Ligands Antigens Deep Learning Signal Transduction Humans T-Lymphocytes Epitopes, T-Lymphocyte Receptors, Antigen, T-Cell Databases, Protein Software Peptides Protein Binding
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