AIMC Topic: Ligands

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Recognizing five molecular ligand-binding sites with similar chemical structure.

Journal of computational chemistry Oct 23, 2019
Accurate identification of ligand-binding sites and discovering the protein-ligand interaction mechanism are important for understanding proteins' functions and designing new drugs. Meanwhile, accurate computational prediction and mechanism research ...
Support Vector Machine Proteins Adenosine Triphosphate Guanosine Triphosphate Adenosine Diphosphate Guanosine Diphosphate Ligands Binding Sites NAD
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Computational methods and tools for binding site recognition between proteins and small molecules: from classical geometrical approaches to modern machine learning strategies.

Journal of computer-aided molecular design Oct 18, 2019
In the current "genomic era" the number of identified genes is growing exponentially. However, the biological function of a large number of the corresponding proteins is still unknown. Recognition of small molecule ligands (e.g., substrates, inhibito...
Protein Conformation Small Molecule Libraries Sequence Homology Protein Binding Computational Biology Proteins Machine Learning Models, Molecular Humans Software Amino Acid Sequence Artificial Intelligence Drug Design 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 Drug Development Proteins Ligands
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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...
Reproducibility of Results Drug Discovery Deep Learning Algorithms Ligands Small Molecule Libraries Quantitative Structure-Activity Relationship Constitutive Androstane Receptor Receptors, Cytoplasmic and Nuclear
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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...
Datasets as Topic Factor Xa Hydrogen Bonding Static Electricity X-Linked Inhibitor of Apoptosis Protein Organic Chemicals Quantum Theory Ligands Deep Learning Protein Binding Drug Discovery
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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...
Quantitative Structure-Activity Relationship Small Molecule Libraries Models, Chemical Ligands Humans Molecular Conformation Drug Discovery Neural Networks, Computer Proteins Drug Design
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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...
Neural Networks, Computer Molecular Targeted Therapy Protein Conformation Computer Graphics Proteins Algorithms Models, Molecular Computational Biology Ligands
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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 Receptor, Cannabinoid, CB1 Machine Learning Computer Simulation Computational Biology Ligands
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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 Molecular Conformation Models, Molecular Ligands 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...
Ligands Proteins Deep Learning Databases, Pharmaceutical Pharmaceutical Preparations User-Computer Interface Drug Evaluation, Preclinical
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