AIMC Topic: Structure-Activity Relationship

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Bioactivity and structure-activity relationship of cinnamic acid derivatives and its heteroaromatic ring analogues as potential high-efficient acaricides against Psoroptes cuniculi.

Bioorganic & medicinal chemistry letters Aug 24, 2017
A series of cinnamic acid derivatives and its heteroaromatic ring analogues were synthesized and evaluated for acaricidal activity in vitro against Psoroptes cuniculi, a mange mite. Among them, eight compounds showed the higher activity with median l...
Psoroptidae Molecular Structure Animals Cinnamates Dose-Response Relationship, Drug Acaricides Structure-Activity Relationship
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Bias-Free Chemically Diverse Test Sets from Machine Learning.

ACS combinatorial science Jul 27, 2017
Current benchmarking methods in quantum chemistry rely on databases that are built using a chemist's intuition. It is not fully understood how diverse or representative these databases truly are. Multivariate statistical techniques like archetypal an...
Databases, Chemical Thermodynamics Machine Learning Cluster Analysis Models, Chemical Quantum Theory Structure-Activity Relationship Principal Component Analysis
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Machine-Learning-Assisted Approach for Discovering Novel Inhibitors Targeting Bromodomain-Containing Protein 4.

Journal of chemical information and modeling Jul 10, 2017
Bromodomain-containing protein 4 (BRD4) is implicated in the pathogenesis of a number of different cancers, inflammatory diseases and heart failure. Much effort has been dedicated toward discovering novel scaffold BRD4 inhibitors (BRD4is) with differ...
Machine Learning Molecular Docking Simulation Drug Discovery Nuclear Proteins Humans Drug Evaluation, Preclinical Molecular Targeted Therapy Protein Conformation Cell Cycle Proteins Transcription Factors Cell Line Structure-Activity Relationship
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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...
Humans Protein Binding Computational Biology Databases, Protein Antibodies, Monoclonal Neural Networks, Computer Machine Learning Peptides Histocompatibility Antigens Class II Ligands Structure-Activity Relationship Binding Sites Mutation Protein Interaction Domains and Motifs Epitopes
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Novel Imidazo[4,5-c][1,2,6]thiadiazine 2,2-dioxides as antiproliferative trypanosoma cruzi drugs: Computational screening from neural network, synthesis and in vivo biological properties.

European journal of medicinal chemistry May 1, 2017
A new family of imidazo[4,5-c][1,2,6]thiadiazine 2,2-dioxide with antiproliferative Trypanosoma cruzi properties was identified from a neural network model published by our group. The synthesis and evaluation of this new class of trypanocidal agents ...
Molecular Structure Trypanosoma cruzi Thiadiazines Dose-Response Relationship, Drug Imidazoles Structure-Activity Relationship Neural Networks, Computer Macrophages Cell Proliferation Fibroblasts Animals Mice
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Improved prediction of protein-protein interactions using novel negative samples, features, and an ensemble classifier.

Artificial intelligence in medicine Mar 4, 2017
Computational methods are employed in bioinformatics to predict protein-protein interactions (PPIs). PPIs and protein-protein non-interactions (PPNIs) display different levels of development, and the number of PPIs is considerably greater than that o...
Protein Structure, Secondary Structure-Activity Relationship Natural Language Processing Amino Acid Sequence Computational Biology Reproducibility of Results Proteins Protein Interaction Maps Databases, Protein Humans
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Study of Structure-active Relationship for Inhibitors of HIV-1 Integrase LEDGF/p75 Interaction by Machine Learning Methods.

Molecular informatics Feb 28, 2017
HIV-1 integrase (IN) is a promising target for anti-AIDS therapy, and LEDGF/p75 is proved to enhance the HIV-1 integrase strand transfer activity in vitro. Blocking the interaction between IN and LEDGF/p75 is an effective way to inhibit HIV replicati...
Machine Learning Computer Simulation Reproducibility of Results Structure-Activity Relationship Humans Models, Molecular Molecular Conformation ROC Curve Molecular Structure HIV Integrase Protein Binding Intercellular Signaling Peptides and Proteins HIV Integrase Inhibitors
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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 ...
Structure-Activity Relationship Machine Learning Receptors, Serotonin Ligands Drug Discovery Informatics
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Machine learning and docking models for Mycobacterium tuberculosis topoisomerase I.

Tuberculosis (Edinburgh, Scotland) Jan 20, 2017
There is a shortage of compounds that are directed towards new targets apart from those targeted by the FDA approved drugs used against Mycobacterium tuberculosis. Topoisomerase I (Mttopo I) is an essential mycobacterial enzyme and a promising target...
Tuberculosis Humans Mycobacterium tuberculosis Machine Learning Mycobacterium smegmatis Molecular Targeted Therapy Molecular Docking Simulation Structure-Activity Relationship Drug Design DNA Topoisomerases, Type I Protein Conformation Computer-Aided Design Bayes Theorem Dose-Response Relationship, Drug Topoisomerase I Inhibitors Antitubercular Agents
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Nano Random Forests to mine protein complexes and their relationships in quantitative proteomics data.

Molecular biology of the cell Jan 5, 2017
Ever-increasing numbers of quantitative proteomics data sets constitute an underexploited resource for investigating protein function. Multiprotein complexes often follow consistent trends in these experiments, which could provide insights about thei...
Multiprotein Complexes Kinetochores DNA-Binding Proteins Datasets as Topic Machine Learning Adenosine Triphosphatases Proteomics Structure-Activity Relationship Computer Simulation
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