AIMC Topic: Drug Design

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Cheminformatics Based Machine Learning Approaches for Assessing Glycolytic Pathway Antagonists of Mycobacterium tuberculosis.

Combinatorial chemistry & high throughput screening Jan 1, 2016
BACKGROUND: Tuberculosis is the second leading cause of death from an infectious disease worldwide after HIV, thus reasoning the expeditions in antituberculosis research. The rising number of cases of infection by resistant forms of M. tuberculosis h...
Enzyme Inhibitors Fructose-Bisphosphate Aldolase Mycobacterium tuberculosis High-Throughput Screening Assays Machine Learning Algorithms Drug Design Glycolysis Computational Biology Tuberculosis Humans
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Discrimination of Active and Weakly Active Human BACE1 Inhibitors Using Self-Organizing Map and Support Vector Machine.

Combinatorial chemistry & high throughput screening Jan 1, 2016
β-secretase (BACE1) is an aspartyl protease, which is considered as a novel vital target in Alzheimer`s disease therapy. We collected a data set of 294 BACE1 inhibitors, and built six classification models to discriminate active and weakly active inh...
Amyloid Precursor Protein Secretases Aspartic Acid Endopeptidases Sensitivity and Specificity Drug Design Support Vector Machine Alzheimer Disease Enzyme Inhibitors Linear Models Humans
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Using Deep Learning for Compound Selectivity Prediction.

Current computer-aided drug design Jan 1, 2016
Compound selectivity prediction plays an important role in identifying potential compounds that bind to the target of interest with high affinity. However, there is still short of efficient and accurate computational approaches to analyze and predict...
Neural Networks, Computer Machine Learning Algorithms Computer-Aided Design Drug Design Probability
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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...
Humans Neural Networks, Computer Machine Learning Models, Biological Ligands Binding Sites Molecular Docking Simulation Drug Discovery Drug Design Support Vector Machine Decision Trees
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Artificial Neural Network Analysis of Pharmacokinetic and Toxicity Properties of Lead Molecules for Dengue Fever, Tuberculosis and Malaria.

Current computer-aided drug design Jan 1, 2016
Poor pharmacokinetic and toxicity profiles are major reasons for the low rate of advancing lead drug candidates into efficacy studies. The In-silico prediction of primary pharmacokinetic and toxicity properties in the drug discovery and development p...
Humans Neural Networks, Computer Anti-Infective Agents Drug Design Dengue Mice Ether-A-Go-Go Potassium Channels Malaria Plasmodium Computer Simulation Tuberculosis Animals Dengue Virus Computer-Aided Design Mycobacterium
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Design, Synthesis and Biological Evaluation of New Thieno[2,3- d]pyrimidines as Anti-inflammatory Agents.

Anti-inflammatory & anti-allergy agents in medicinal chemistry Jan 1, 2015
BACKGROUND: Long term use of NSAIDS is mainly accompanied by major health implications such as gastrointestinal erosions, ulcerations and nephrotoxicity. These side effects arise from local irritation by the carboxylic acid moiety, that is common to ...
Anti-Inflammatory Agents Drug Design Carrageenan Dinoprostone Edema Animals Rats, Wistar Pyrimidines Male
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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...
Ligands Receptor, Metabotropic Glutamate 5 Receptor, Serotonin, 5-HT2B Drug Design Protein Binding Receptors, Opioid, mu Humans Neural Networks, Computer
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Bio-AIMS Collection of Chemoinformatics Web Tools based on Molecular Graph Information and Artificial Intelligence Models.

Combinatorial chemistry & high throughput screening Jan 1, 2015
The molecular information encoding into molecular descriptors is the first step into in silico Chemoinformatics methods in Drug Design. The Machine Learning methods are a complex solution to find prediction models for specific biological properties o...
Proteins Models, Biological Quantitative Structure-Activity Relationship Artificial Intelligence Informatics Drug Design
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A Study of Applications of Machine Learning Based Classification Methods for Virtual Screening of Lead Molecules.

Combinatorial chemistry & high throughput screening Jan 1, 2015
The ligand-based virtual screening of combinatorial libraries employs a number of statistical modeling and machine learning methods. A comprehensive analysis of the application of these methods for the diversity oriented virtual screening of biologic...
Hypoglycemic Agents Anticonvulsants Arrhythmias, Cardiac Antidepressive Agents Anti-Bacterial Agents Drug Delivery Systems Drug Design Machine Learning
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On the Origins of Hepatitis C Virus NS5B Polymerase Inhibitory Activity Using Machine Learning Approaches.

Current topics in medicinal chemistry Jan 1, 2015
Inhibition of non-structural protein 5B (NS5B) represents an attractive strategy for the therapeutic treatment of hepatitis C virus (HCV). In this study, machine learning classifiers such as artificial neural network (ANN), support vector machine (SV...
Machine Learning Drug Design Enzyme Inhibitors Structure-Activity Relationship Viral Nonstructural Proteins Molecular Structure High-Throughput Screening Assays
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