AI Medical Compendium Topic:
Ligands

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Chemogenomic Active Learning's Domain of Applicability on Small, Sparse qHTS Matrices: A Study Using Cytochrome P450 and Nuclear Hormone Receptor Families.

ChemMedChem Feb 5, 2018
Computational models for predicting the activity of small molecules against targets are now routinely developed and used in academia and industry, partially due to public bioactivity databases. While models based on bigger datasets are the trend, rec...
Humans Machine Learning Cytochrome P-450 Enzyme System Receptors, Cytoplasmic and Nuclear Small Molecule Libraries Cytochrome P-450 Enzyme Inhibitors Algorithms Databases, Factual Ligands
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Boosted neural networks scoring functions for accurate ligand docking and ranking.

Journal of bioinformatics and computational biology Feb 4, 2018
Predicting the native poses of ligands correctly is one of the most important steps towards successful structure-based drug design. Binding affinities (BAs) estimated by traditional scoring functions (SFs) are typically used to score and rank-order p...
Drug Design Neural Networks, Computer Ligands Databases, Protein Protein Binding tRNA Methyltransferases Algorithms Escherichia coli Proteins Molecular Docking Simulation Structure-Activity Relationship Machine Learning Proteins
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Development of CDK-targeted scoring functions for prediction of binding affinity.

Biophysical chemistry Feb 1, 2018
Cyclin-dependent kinase (CDK) is an interesting biological macromolecule due to its role in cell cycle progression, transcription control, and neuronal development, to mention the most studied biological activities. Furthermore, the availability of h...
Binding Sites Cyclin-Dependent Kinases Machine Learning Ligands
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Prediction of GPCR-Ligand Binding Using Machine Learning Algorithms.

Computational and mathematical methods in medicine Jan 30, 2018
We propose a novel method that predicts binding of G-protein coupled receptors (GPCRs) and ligands. The proposed method uses hub and cycle structures of ligands and amino acid motif sequences of GPCRs, rather than the 3D structure of a receptor or si...
Binding Sites Amino Acid Motifs Sequence Analysis, Protein Quercetin Software Ligands Algorithms False Positive Reactions ROC Curve Humans Reproducibility of Results Protein Binding Machine Learning Receptors, G-Protein-Coupled Area Under Curve
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K: Protein-Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks.

Journal of chemical information and modeling Jan 29, 2018
Accurately predicting protein-ligand binding affinities is an important problem in computational chemistry since it can substantially accelerate drug discovery for virtual screening and lead optimization. We propose here a fast machine-learning appro...
Models, Chemical Ligands Drug Discovery Deep Learning Protein Binding Databases, Protein Structure-Activity Relationship Computational Biology Proteins
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Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening.

PLoS computational biology Jan 8, 2018
This work introduces a number of algebraic topology approaches, including multi-component persistent homology, multi-level persistent homology, and electrostatic persistence for the representation, characterization, and description of small molecules...
Neural Networks, Computer Machine Learning Algorithms Protein Binding Humans Databases, Protein Area Under Curve Molecular Dynamics Simulation Ligands Computational Biology Models, Neurological Proteins Nucleic Acids Static Electricity Protein Interaction Mapping
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Coupling Matched Molecular Pairs with Machine Learning for Virtual Compound Optimization.

Journal of chemical information and modeling Nov 27, 2017
Matched molecular pair (MMP) analyses are widely used in compound optimization projects to gain insights into structure-activity relationships (SAR). The analysis is traditionally done via statistical methods but can also be employed together with ma...
Humans Machine Learning Models, Biological Computer Simulation Ligands Drug Discovery Structure-Activity Relationship
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Modern drug design: the implication of using artificial neuronal networks and multiple molecular dynamic simulations.

Journal of computer-aided molecular design Nov 13, 2017
We report the implementation of molecular modeling approaches developed as a part of the 2016 Grand Challenge 2, the blinded competition of computer aided drug design technologies held by the D3R Drug Design Data Resource ( https://drugdesigndata.org...
Molecular Dynamics Simulation Ligands Protein Conformation Receptors, Cytoplasmic and Nuclear Binding Sites Molecular Docking Simulation Computer-Aided Design Drug Design Protein Binding Humans Neural Networks, Computer Machine Learning
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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) ...
Drug Design Antineoplastic Agents Datasets as Topic ROC Curve Supervised Machine Learning Databases, Protein Humans Thermodynamics Protein Kinase Inhibitors Ligands Molecular Docking Simulation Cyclin-Dependent Kinase 2 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...
Binding Sites Proteins Databases, Protein Molecular Docking Simulation Structure-Activity Relationship Ligands Molecular Structure Drug Design Humans Protein Conformation Protein Binding Knowledge Bases Machine Learning Algorithms
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