AIMC Topic: 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...
Molecular Docking Simulation Protein Binding Receptors, G-Protein-Coupled Neural Networks, Computer Software Retrospective Studies Ligands
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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 Drug Design Databases, Protein 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...
Deep Learning Antigens Receptors, Antigen, T-Cell Signal Transduction Binding Sites T-Lymphocytes Humans Protein Binding Databases, Protein Protein Interaction Domains and Motifs Software Epitopes, T-Lymphocyte Ligands Peptides
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A machine learning-based chemoproteomic approach to identify drug targets and binding sites in complex proteomes.

Nature communications Aug 21, 2020
Chemoproteomics is a key technology to characterize the mode of action of drugs, as it directly identifies the protein targets of bioactive compounds and aids in the development of optimized small-molecule compounds. Current approaches cannot identif...
Botrytis Proteolysis Binding Sites Phosphotransferases Proteome Mass Spectrometry Cell Survival Ligands HeLa Cells Saccharomyces cerevisiae Protein Binding Humans Computational Biology Drug Delivery Systems Machine Learning Proteomics Drug Discovery
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Improved Scaffold Hopping in Ligand-Based Virtual Screening Using Neural Representation Learning.

Journal of chemical information and modeling Aug 19, 2020
Deep learning has demonstrated significant potential in advancing state of the art in many problem domains, especially those benefiting from automated feature extraction. Yet, the methodology has seen limited adoption in the field of ligand-based vir...
Neural Networks, Computer Algorithms Databases, Factual Prospective Studies Ligands
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Comprehensive Prediction of Molecular Recognition in a Combinatorial Chemical Space Using Machine Learning.

ACS combinatorial science Aug 17, 2020
In combinatorial chemical approaches, optimizing the composition and arrangement of building blocks toward a particular function has been done using a number of methods, including high throughput molecular screening, molecular evolution, and computat...
Machine Learning Ligands Peptides High-Throughput Screening Assays Protein Binding Models, Molecular Combinatorial Chemistry Techniques Peptide Library Molecular Structure Structure-Activity Relationship Amino Acid Sequence
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Unbiased Characterization of Peptide-HLA Class II Interactions Based on Large-Scale Peptide Microarrays; Assessment of the Impact on HLA Class II Ligand and Epitope Prediction.

Frontiers in immunology Aug 5, 2020
Human Leukocyte Antigen class II (HLA-II) molecules present peptides to T lymphocytes and play an important role in adaptive immune responses. Characterizing the binding specificity of single HLA-II molecules has profound impacts for understanding ce...
Humans Histocompatibility Antigens Class II Machine Learning Antigen Presentation Epitopes, T-Lymphocyte Binding Sites Ligands CD4 Antigens Protein Binding High-Throughput Screening Assays Protein Interaction Domains and Motifs Databases, Protein Protein Array Analysis HLA Antigens
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Data Set Augmentation Allows Deep Learning-Based Virtual Screening to Better Generalize to Unseen Target Classes and Highlight Important Binding Interactions.

Journal of chemical information and modeling Aug 4, 2020
Current deep learning methods for structure-based virtual screening take the structures of both the protein and the ligand as input but make little or no use of the protein structure when predicting ligand binding. Here, we show how a relatively simp...
Deep Learning Proteins Ligands
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Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition.

Methods (San Diego, Calif.) Jul 6, 2020
In the last decade, machine learning and artificial intelligence applications have received a significant boost in performance and attention in both academic research and industry. The success behind most of the recent state-of-the-art methods can be...
Neural Networks, Computer Artificial Intelligence Machine Learning Software Drug Discovery Deep Learning Receptors, G-Protein-Coupled Supervised Machine Learning Ligands
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Identification of ligand-binding residues using protein sequence profile alignment and query-specific support vector machine model.

Analytical biochemistry Jul 2, 2020
Information embedded in ligand-binding residues (LBRs) of proteins is important for understanding protein functions. How to accurately identify the potential ligand-binding residues is still a challenging problem, especially only protein sequence is ...
Databases, Protein Binding Sites Protein Binding Amino Acid Sequence Support Vector Machine Ligands Proteins
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