AIMC Topic: Ligands

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Graph Convolutional Neural Networks for Predicting Drug-Target Interactions.

Journal of chemical information and modeling Oct 16, 2019
Accurate determination of target-ligand interactions is crucial in the drug discovery process. In this paper, we propose a graph-convolutional (Graph-CNN) framework for predicting protein-ligand interactions. First, we built an unsupervised graph-aut...
Neural Networks, Computer Drug Discovery Protein Binding Databases, Chemical Protein Conformation Binding Sites Proteins Ligands Drug Development
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Prediction Model with High-Performance Constitutive Androstane Receptor (CAR) Using DeepSnap-Deep Learning Approach from the Tox21 10K Compound Library.

International journal of molecular sciences Sep 30, 2019
The constitutive androstane receptor (CAR) plays pivotal roles in drug-induced liver injury through the transcriptional regulation of drug-metabolizing enzymes and transporters. Thus, identifying regulatory factors for CAR activation is important for...
Algorithms Small Molecule Libraries Constitutive Androstane Receptor Receptors, Cytoplasmic and Nuclear Ligands Quantitative Structure-Activity Relationship Reproducibility of Results Deep Learning Drug Discovery
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Practical High-Quality Electrostatic Potential Surfaces for Drug Discovery Using a Graph-Convolutional Deep Neural Network.

Journal of medicinal chemistry Sep 25, 2019
Inspecting protein and ligand electrostatic potential (ESP) surfaces in order to optimize electrostatic complementarity is a key activity in drug design. These ESP surfaces need to reflect the true electrostatic nature of the molecules, which typical...
Hydrogen Bonding Static Electricity Ligands X-Linked Inhibitor of Apoptosis Protein Deep Learning Datasets as Topic Factor Xa Protein Binding Organic Chemicals Drug Discovery Quantum Theory
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From Target to Drug: Generative Modeling for the Multimodal Structure-Based Ligand Design.

Molecular pharmaceutics Sep 10, 2019
Chemical space is impractically large, and conventional structure-based virtual screening techniques cannot be used to simply search through the entire space to discover effective bioactive molecules. To address this shortcoming, we propose a generat...
Neural Networks, Computer Drug Design Drug Discovery Humans Small Molecule Libraries Ligands Molecular Conformation Quantitative Structure-Activity Relationship Proteins Models, Chemical
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Predicting Drug-Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation.

Journal of chemical information and modeling Sep 6, 2019
We propose a novel deep learning approach for predicting drug-target interaction using a graph neural network. We introduce a distance-aware graph attention algorithm to differentiate various types of intermolecular interactions. Furthermore, we extr...
Models, Molecular Ligands Molecular Targeted Therapy Computational Biology Proteins Computer Graphics Protein Conformation Neural Networks, Computer Algorithms
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Prediction and Experimental Confirmation of Novel Peripheral Cannabinoid-1 Receptor Antagonists.

Journal of chemical information and modeling Sep 4, 2019
Small molecules targeting peripheral CB1 receptors have therapeutic potential in a variety of disorders including obesity-related, hormonal, and metabolic abnormalities, while avoiding the psychoactive effects in the central nervous system. We applie...
Small Molecule Libraries Machine Learning Receptor, Cannabinoid, CB1 Computational Biology Ligands Computer Simulation
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Enhanced Deep-Learning Prediction of Molecular Properties via Augmentation of Bond Topology.

ChemMedChem Aug 23, 2019
Deep learning has made great strides in tackling chemical problems, but still lacks full-fledged representations for three-dimensional (3D) molecular structures for its inner working. For example, the molecular graph, commonly used in chemistry and r...
Deep Learning Models, Molecular Ligands Molecular Conformation Datasets as Topic
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Hidden bias in the DUD-E dataset leads to misleading performance of deep learning in structure-based virtual screening.

PloS one Aug 20, 2019
Recently much effort has been invested in using convolutional neural network (CNN) models trained on 3D structural images of protein-ligand complexes to distinguish binding from non-binding ligands for virtual screening. However, the dearth of reliab...
Proteins Ligands Deep Learning Pharmaceutical Preparations User-Computer Interface Databases, Pharmaceutical Drug Evaluation, Preclinical
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Target-Specific Prediction of Ligand Affinity with Structure-Based Interaction Fingerprints.

Journal of chemical information and modeling Aug 19, 2019
Discovery and optimization of small molecule inhibitors as therapeutic drugs have immensely benefited from rational structure-based drug design. With recent advances in high-resolution structure determination, computational power, and machine learnin...
HIV-1 Humans Ligands Machine Learning HIV Protease Inhibitors Drug Discovery HIV Protease HIV Infections Molecular Docking Simulation Protein Binding Drug Design
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Significance of Data Selection in Deep Learning for Reliable Binding Mode Prediction of Ligands in the Active Site of CYP3A4.

Chemical & pharmaceutical bulletin Aug 17, 2019
For rational drug design, it is essential to predict the binding mode of protein-ligand complexes. Although various machine learning-based models have been reported that use convolutional neural networks (deep learning) to predict binding modes from ...
Binding Sites Databases, Factual Ligands Deep Learning Cytochrome P-450 CYP3A Humans
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