AIMC Topic: Ligands

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Supervised machine learning techniques to predict binding affinity. A study for cyclin-dependent kinase 2.

Biochemical and biophysical research communications Oct 7, 2017
Here we report the development of a machine-learning model to predict binding affinity based on the crystallographic structures of protein-ligand complexes. We used an ensemble of crystallographic structures (resolution better than 1.5 Å resolution) ...
Antineoplastic Agents ROC Curve Databases, Protein Humans Molecular Docking Simulation Drug Design Datasets as Topic Protein Kinase Inhibitors Thermodynamics Cyclin-Dependent Kinase 2 Supervised Machine Learning Ligands Inhibitory Concentration 50
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Convex-PL: a novel knowledge-based potential for protein-ligand interactions deduced from structural databases using convex optimization.

Journal of computer-aided molecular design Sep 18, 2017
We present a novel optimization approach to train a free-shape distance-dependent protein-ligand scoring function called Convex-PL. We do not impose any functional form of the scoring function. Instead, we decompose it into a polynomial basis and ded...
Humans Machine Learning Algorithms Structure-Activity Relationship Ligands Protein Binding Molecular Structure Protein Conformation Proteins Molecular Docking Simulation Knowledge Bases Binding Sites Databases, Protein Drug Design
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Integration of element specific persistent homology and machine learning for protein-ligand binding affinity prediction.

International journal for numerical methods in biomedical engineering Aug 16, 2017
Protein-ligand binding is a fundamental biological process that is paramount to many other biological processes, such as signal transduction, metabolic pathways, enzyme construction, cell secretion, and gene expression. Accurate prediction of protein...
Machine Learning Protein Binding Hydrogen Bonding Proteins Databases, Protein Ligands
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Machine learning reveals a non-canonical mode of peptide binding to MHC class II molecules.

Immunology Jun 19, 2017
MHC class II molecules play a fundamental role in the cellular immune system: they load short peptide fragments derived from extracellular proteins and present them on the cell surface. It is currently thought that the peptide binds lying more or les...
Histocompatibility Antigens Class II Ligands Binding Sites Protein Interaction Domains and Motifs Computational Biology Databases, Protein Antibodies, Monoclonal Structure-Activity Relationship Mutation Epitopes Machine Learning Peptides Protein Binding Humans Neural Networks, Computer
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Protein-Ligand Scoring with Convolutional Neural Networks.

Journal of chemical information and modeling Apr 11, 2017
Computational approaches to drug discovery can reduce the time and cost associated with experimental assays and enable the screening of novel chemotypes. Structure-based drug design methods rely on scoring functions to rank and predict binding affini...
Ligands Proteins Protein Conformation Neural Networks, Computer Models, Molecular User-Computer Interface Drug Evaluation, Preclinical Computational Biology
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Structural and Sequence Similarity Makes a Significant Impact on Machine-Learning-Based Scoring Functions for Protein-Ligand Interactions.

Journal of chemical information and modeling Apr 5, 2017
The prediction of protein-ligand binding affinity has recently been improved remarkably by machine-learning-based scoring functions. For example, using a set of simple descriptors representing the atomic distance counts, the RF-Score improves the Pea...
Ligands Machine Learning Proteins Amino Acid Sequence Protein Binding Computational Biology
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MOST: most-similar ligand based approach to target prediction.

BMC bioinformatics Mar 11, 2017
BACKGROUND: Many computational approaches have been used for target prediction, including machine learning, reverse docking, bioactivity spectra analysis, and chemical similarity searching. Recent studies have suggested that chemical similarity searc...
Cathartics Aloe Humans Machine Learning Animals Acetylcholinesterase Logistic Models Emodin Kinetics Databases, Chemical Ligands
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Active learning for computational chemogenomics.

Future medicinal chemistry Mar 6, 2017
AIM: Computational chemogenomics models the compound-protein interaction space, typically for drug discovery, where existing methods predominantly either incorporate increasing numbers of bioactivity samples or focus on specific subfamilies of protei...
Computational Biology Proteins Models, Chemical Ligands Computer Simulation Databases, Chemical Drug Discovery Machine Learning Binding Sites Genomics
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A Hybrid Knowledge-Based and Empirical Scoring Function for Protein-Ligand Interaction: SMoG2016.

Journal of chemical information and modeling Feb 27, 2017
We present the third generation of our scoring function for the prediction of protein-ligand binding free energy. This function is now a hybrid between a knowledge-based potential and an empirical function. We constructed a diversified set of ∼1000 c...
Probability Protein Conformation Entropy Protein Binding Models, Molecular Proteins Knowledge Bases Databases, Protein Ligands
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Practical application of the Average Information Content Maximization (AIC-MAX) algorithm: selection of the most important structural features for serotonin receptor ligands.

Molecular diversity Feb 9, 2017
The Average Information Content Maximization algorithm (AIC-MAX) based on mutual information maximization was recently introduced to select the most discriminatory features. Here, this methodology was applied to select the most significant bits from ...
Ligands Machine Learning Informatics Drug Discovery Receptors, Serotonin Structure-Activity Relationship
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