AIMC Topic: Ligands

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NNAlign: a platform to construct and evaluate artificial neural network models of receptor-ligand interactions.

Nucleic acids research Jul 3, 2017
Peptides are extensively used to characterize functional or (linear) structural aspects of receptor-ligand interactions in biological systems, e.g. SH2, SH3, PDZ peptide-recognition domains, the MHC membrane receptors and enzymes such as kinases and ...
Amino Acid Sequence Ligands Cell Cycle Proteins Basic Helix-Loop-Helix Leucine Zipper Transcription Factors Internet Forkhead Transcription Factors HLA-A1 Antigen HLA-B7 Antigen HLA-B8 Antigen HLA-DRB1 Chains Binding Sites Trans-Activators Sequence Alignment Neural Networks, Computer Software Algorithms Peptides Protein Binding Databases, Protein Humans
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Optimized Virtual Screening Workflow: Towards Target-Based Polynomial Scoring Functions for HIV-1 Protease.

Combinatorial chemistry & high throughput screening Jan 1, 2017
BACKGROUND: One key step in the development of inhibitors for an enzyme is the application of computational methodologies to predict protein-ligand interactions. The abundance of structural and ligand-binding information for HIV-1 protease opens up t...
HIV Protease Inhibitors Drug Evaluation, Preclinical Ligands HIV Protease Machine Learning Molecular Docking Simulation
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Supervised Machine Learning Methods Applied to Predict Ligand- Binding Affinity.

Current medicinal chemistry Jan 1, 2017
BACKGROUND: Calculation of ligand-binding affinity is an open problem in computational medicinal chemistry. The ability to computationally predict affinities has a beneficial impact in the early stages of drug development, since it allows a mathemati...
Humans Supervised Machine Learning Ligands Binding Sites
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Some Remarks on Prediction of Drug-Target Interaction with Network Models.

Current topics in medicinal chemistry Jan 1, 2017
System-level understanding of the relationships between drugs and targets is very important for enhancing drug research, especially for drug function repositioning. The experimental methods used to determine drug-target interactions are usually time-...
Neural Networks, Computer Algorithms Molecular Docking Simulation Humans Molecular Targeted Therapy Ion Channels Receptors, G-Protein-Coupled Pharmaceutical Preparations Ligands
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Machine learning-, rule- and pharmacophore-based classification on the inhibition of P-glycoprotein and NorA.

SAR and QSAR in environmental research Sep 1, 2016
The efflux pumps P-glycoprotein (P-gp) in humans and NorA in Staphylococcus aureus are of great interest for medicinal chemists because of their important roles in multidrug resistance (MDR). The high polyspecificity as well as the unavailability of ...
Ligands Databases, Chemical Quantitative Structure-Activity Relationship Multidrug Resistance-Associated Proteins Staphylococcus aureus Bacterial Proteins ATP Binding Cassette Transporter, Subfamily B, Member 1 Machine Learning Algorithms Drug Discovery Humans
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Use of machine learning approaches for novel drug discovery.

Expert opinion on drug discovery Jan 1, 2016
INTRODUCTION: The use of computational tools in the early stages of drug development has increased in recent decades. Machine learning (ML) approaches have been of special interest, since they can be applied in several steps of the drug discovery met...
Support Vector Machine Molecular Docking Simulation Drug Design Decision Trees Humans Models, Biological Drug Discovery Neural Networks, Computer Ligands Machine Learning Binding Sites
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Analysis of Drug Design for a Selection of G Protein-Coupled Neuro- Receptors Using Neural Network Techniques.

Current computer-aided drug design Jan 1, 2015
A study is presented on how well possible drug-molecules can be predicted with respect to their function and binding to a selection of neuro-receptors by the use of artificial neural networks. The ligands investigated in this study are chosen to be c...
Receptor, Serotonin, 5-HT2B Receptors, Opioid, mu Humans Neural Networks, Computer Protein Binding Drug Design Ligands Receptor, Metabotropic Glutamate 5
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Constructing query-driven dynamic machine learning model with application to protein-ligand binding sites prediction.

IEEE transactions on nanobioscience Jan 1, 2015
We are facing an era with annotated biological data rapidly and continuously generated. How to effectively incorporate new annotated data into the learning step is crucial for enhancing the performance of a bioinformatics prediction model. Although m...
Binding Sites Machine Learning Protein Binding Models, Biological Proteins Nucleotides Databases, Protein Ligands
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Compressed images for affinity prediction-2 (CIFAP-2): an improved machine learning methodology on protein-ligand interactions based on a study on caspase 3 inhibitors.

Journal of enzyme inhibition and medicinal chemistry Jan 1, 2015
The aim of this study is to propose an improved computational methodology, which is called Compressed Images for Affinity Prediction-2 (CIFAP-2) to predict binding affinities of structurally related protein-ligand complexes. CIFAP-2 method is establi...
Humans Machine Learning Structure-Activity Relationship Molecular Structure Ligands Sulfonamides Enzyme Inhibitors Dose-Response Relationship, Drug Caspase 3 Regression Analysis
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Ligand biological activity predictions using fingerprint-based artificial neural networks (FANN-QSAR).

Methods in molecular biology (Clifton, N.J.) Jan 1, 2015
This chapter focuses on the fingerprint-based artificial neural networks QSAR (FANN-QSAR) approach to predict biological activities of structurally diverse compounds. Three types of fingerprints, namely ECFP6, FP2, and MACCS, were used as inputs to t...
Models, Molecular Algorithms Ligands Quantitative Structure-Activity Relationship Protein Binding Databases, Chemical Cannabinoids Neural Networks, Computer Computational Biology Receptors, Cannabinoid
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