AIMC Topic: Ligands

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Classification of thyroid hormone receptor agonists and antagonists using statistical learning approaches.

Molecular diversity Jul 16, 2018
In silico models are presented for modeling and predicting thyroid hormone receptor (TR) agonists and antagonists. A data set consisting of 258 compounds is used in the present work. The C4.5, random forest (RF) and support vector machine (SVM) stati...
Drug Discovery Support Vector Machine Quantitative Structure-Activity Relationship Receptors, Thyroid Hormone Ligands Algorithms
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Convolutional neural network scoring and minimization in the D3R 2017 community challenge.

Journal of computer-aided molecular design Jul 10, 2018
We assess the ability of our convolutional neural network (CNN)-based scoring functions to perform several common tasks in the domain of drug discovery. These include correctly identifying ligand poses near and far from the true binding mode when giv...
Molecular Docking Simulation Drug Discovery Protein Binding Neural Networks, Computer Protein Conformation Binding Sites Structure-Activity Relationship Algorithms Databases, Protein Cathepsins Ligands
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Neural network and deep-learning algorithms used in QSAR studies: merits and drawbacks.

Drug discovery today Jun 21, 2018
Pharmaceutical Preparations Ligands Quantitative Structure-Activity Relationship Algorithms Drug Discovery Deep Learning Humans Neural Networks, Computer
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Visualizing convolutional neural network protein-ligand scoring.

Journal of molecular graphics & modelling Jun 18, 2018
Protein-ligand scoring is an important step in a structure-based drug design pipeline. Selecting a correct binding pose and predicting the binding affinity of a protein-ligand complex enables effective virtual screening. Machine learning techniques c...
Protein Binding Proteins ROC Curve Ligands Neural Networks, Computer Molecular Conformation Algorithms Models, Molecular Deep Learning
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Development of Ligand-based Big Data Deep Neural Network Models for Virtual Screening of Large Compound Libraries.

Molecular informatics Jun 8, 2018
High-performance ligand-based virtual screening (VS) models have been developed using various computational methods, including the deep neural network (DNN) method. There are high expectations for exploration of the advanced capabilities of DNN to im...
Molecular Docking Simulation Drug Discovery Big Data Humans Neural Networks, Computer ErbB Receptors Small Molecule Libraries Ligands Databases, Chemical
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Development of a machine-learning model to predict Gibbs free energy of binding for protein-ligand complexes.

Biophysical chemistry Jun 7, 2018
The possibility of using the atomic coordinates of protein-ligand complexes to assess binding affinity has a beneficial impact in the early stages of drug development and design. From the computational view, the creation of reliable scoring functions...
Databases, Protein Ligands Thermodynamics Protein Binding Proteins Machine Learning Molecular Docking Simulation
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Redefining the Protein Kinase Conformational Space with Machine Learning.

Cell chemical biology May 31, 2018
Protein kinases are dynamic, adopting different conformational states that are critical for their catalytic activity. We assess a range of structural features derived from the conserved αC helix and DFG motif to define the conformational space of the...
Humans Protein Conformation Protein Kinases Machine Learning Protein Kinase Inhibitors Models, Molecular Ligands Small Molecule Libraries
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Fingerprint-Based Machine Learning Approach to Identify Potent and Selective 5-HTR Ligands.

Molecules (Basel, Switzerland) May 10, 2018
The identification of subtype-selective GPCR (G-protein coupled receptor) ligands is a challenging task. In this study, we developed a computational protocol to find compounds with 5-HTR versus 5-HTR selectivity. Our approach employs the hierarchical...
Binding Sites Serotonin Receptor, Serotonin, 5-HT2B Models, Molecular Drug Evaluation, Preclinical Ligands Humans Machine Learning
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Most Ligand-Based Classification Benchmarks Reward Memorization Rather than Generalization.

Journal of chemical information and modeling May 8, 2018
Undetected overfitting can occur when there are significant redundancies between training and validation data. We describe AVE, a new measure of training-validation redundancy for ligand-based classification problems, that accounts for the similarity...
Machine Learning Drug Discovery Benchmarking Ligands
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Agonists of G-Protein-Coupled Odorant Receptors Are Predicted from Chemical Features.

The journal of physical chemistry letters Apr 17, 2018
Predicting the activity of chemicals for a given odorant receptor is a longstanding challenge. Here the activity of 258 chemicals on the human G-protein-coupled odorant receptor (OR)51E1, also known as prostate-specific G-protein-coupled receptor 2 (...
Ligands Receptors, G-Protein-Coupled Animals Receptors, Odorant Neoplasm Proteins Drug Evaluation, Preclinical Models, Molecular Protein Binding Humans Fatty Acids Mice Machine Learning
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