AIMC Topic: Ligands

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Artificial intelligence in the early stages of drug discovery.

Archives of biochemistry and biophysics Dec 19, 2020
Although the use of computational methods within the pharmaceutical industry is well established, there is an urgent need for new approaches that can improve and optimize the pipeline of drug discovery and development. In spite of the fact that there...
Pharmaceutical Preparations Ligands Cell Line Proteins Drug Repositioning Humans Animals Drug Discovery Deep Learning Protein Binding
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ChemBoost: A Chemical Language Based Approach for Protein - Ligand Binding Affinity Prediction.

Molecular informatics Dec 14, 2020
Identification of high affinity drug-target interactions is a major research question in drug discovery. Proteins are generally represented by their structures or sequences. However, structures are available only for a small subset of biomolecules an...
Computational Biology Ligands Computational Chemistry Machine Learning Drug Discovery Protein Binding
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Predicting cryptic ligand binding sites based on normal modes guided conformational sampling.

Proteins Dec 5, 2020
To greatly expand the druggable genome, fast and accurate predictions of cryptic sites for small molecules binding in target proteins are in high demand. In this study, we have developed a fast and simple conformational sampling scheme guided by norm...
Humans SARS-CoV-2 ROC Curve Computational Biology Machine Learning Algorithms Reproducibility of Results Karyopherins Protein Tyrosine Phosphatase, Non-Receptor Type 1 Exportin 1 Protein Binding Sites Coronavirus 3C Proteases Coronavirus Papain-Like Proteases Regression Analysis Antigens, Neoplasm Area Under Curve Interleukin-2 Elasticity Molecular Dynamics Simulation Antigens, CD Models, Statistical Protein Conformation Hepacivirus Receptors, Cytoplasmic and Nuclear 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...
Proteins Ligands Drug Design Deep Learning Water Algorithms Molecular Docking Simulation
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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...
Bayes Theorem Ligands Ether-A-Go-Go Potassium Channels Machine Learning Humans Molecular Docking Simulation Models, Molecular
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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...
Aminohydrolases Chemical Phenomena Catalytic Domain Nitriles Protein Binding Bacterial Proteins Ligands Machine Learning Molecular Docking Simulation
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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 Neural Networks, Computer Molecular Docking Simulation Proteins Databases, Protein Deep Learning Ligands Molecular Dynamics Simulation Computer-Aided Design User-Computer Interface Drug Design Protein Binding Drug Discovery
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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...
Neural Networks, Computer Protein Binding Software Proteins Molecular Docking Simulation Ligands
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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...
Models, Molecular Machine Learning Pharmaceutical Preparations Ligands Drug Design Quantum Theory Drug Discovery
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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...
Bayes Theorem Machine Learning Support Vector Machine Ligands Probability
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