AIMC Topic: Drug Evaluation, Preclinical

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Predicting tumor cell line response to drug pairs with deep learning.

BMC bioinformatics Dec 21, 2018
BACKGROUND: The National Cancer Institute drug pair screening effort against 60 well-characterized human tumor cell lines (NCI-60) presents an unprecedented resource for modeling combinational drug activity.
Deep Learning Cell Line, Tumor United States Humans Neural Networks, Computer National Cancer Institute (U.S.) Drug Evaluation, Preclinical
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Practical Model Selection for Prospective Virtual Screening.

Journal of chemical information and modeling Dec 18, 2018
Virtual (computational) high-throughput screening provides a strategy for prioritizing compounds for experimental screens, but the choice of virtual screening algorithm depends on the data set and evaluation strategy. We consider a wide range of liga...
Machine Learning Proteins Drug Evaluation, Preclinical Protein Conformation Molecular Docking Simulation Algorithms User-Computer Interface
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Exploring Alternative Strategies for the Identification of Potent Compounds Using Support Vector Machine and Regression Modeling.

Journal of chemical information and modeling Dec 14, 2018
Support vector regression (SVR) is a premier approach for the prediction of compound potency. Given the conceptual link between support vector machine (SVM) and SVR modeling, SVR is capable of accounting for continuous and discontinuous structure-act...
Regression Analysis Drug Evaluation, Preclinical Quantitative Structure-Activity Relationship Support Vector Machine
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High-throughput brain activity mapping and machine learning as a foundation for systems neuropharmacology.

Nature communications Dec 3, 2018
Technologies for mapping the spatial and temporal patterns of neural activity have advanced our understanding of brain function in both health and disease. An important application of these technologies is the discovery of next-generation neurotherap...
Convulsants Neuropharmacology Zebrafish Animals, Genetically Modified Brain Mapping Molecular Structure Animals Larva Seizures Disease Models, Animal Machine Learning Drug Evaluation, Preclinical Brain Pentylenetetrazole
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Application of Bioactivity Profile-Based Fingerprints for Building Machine Learning Models.

Journal of chemical information and modeling Nov 29, 2018
The volume of high throughput screening data has considerably increased since the beginning of the automated biochemical and cell-based assays era. This information-rich data source provides tremendous repurposing opportunities for data mining. It wa...
Neural Networks, Computer Drug Evaluation, Preclinical High-Throughput Screening Assays Machine Learning
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Predictive classifier models built from natural products with antimalarial bioactivity using machine learning approach.

PloS one Sep 28, 2018
In view of the vast number of natural products with potential antiplasmodial bioactivity and cost of conducting antiplasmodial bioactivity assays, it may be judicious to learn from previous antiplasmodial bioassays and predict bioactivity of these na...
Machine Learning Quantitative Structure-Activity Relationship Workflow Small Molecule Libraries Algorithms Biological Products Drug Evaluation, Preclinical Humans In Vitro Techniques Models, Chemical Antimalarials
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Discovering Highly Potent Molecules from an Initial Set of Inactives Using Iterative Screening.

Journal of chemical information and modeling Sep 10, 2018
The versatility of similarity searching and quantitative structure-activity relationships to model the activity of compound sets within given bioactivity ranges (i.e., interpolation) is well established. However, their relative performance in the com...
Quantitative Structure-Activity Relationship Algorithms Machine Learning Drug Discovery Drug Evaluation, Preclinical
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Using predicate and provenance information from a knowledge graph for drug efficacy screening.

Journal of biomedical semantics Sep 6, 2018
BACKGROUND: Biomedical knowledge graphs have become important tools to computationally analyse the comprehensive body of biomedical knowledge. They represent knowledge as subject-predicate-object triples, in which the predicate indicates the relation...
Computer Graphics ROC Curve Biological Ontologies Drug Evaluation, Preclinical False Negative Reactions
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Integration of virtual screening and susceptibility test to discover active-site subpocket-specific biogenic inhibitors of Helicobacter pylori shikimate dehydrogenase.

International microbiology : the official journal of the Spanish Society for Microbiology Aug 24, 2018
Shikimate dehydrogenase (HpSDH) (EC 1.1.1.25) is a key enzyme in the shikimate pathway of Helicobacter pylori (H. pylori), which catalyzes the NADPH-dependent reversible reduction of 3-dehydroshikimate to shikimate. Targeting HpSDH has been recognize...
Protein Conformation Drug Evaluation, Preclinical Enzyme Inhibitors Alcohol Oxidoreductases Microbial Sensitivity Tests Catalytic Domain Anti-Bacterial Agents Computational Biology Crystallography, X-Ray Molecular Docking Simulation Helicobacter pylori
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RASPELD to Perform High-End Screening in an Academic Environment toward the Development of Cancer Therapeutics.

ChemMedChem Aug 23, 2018
The identification of compounds for dissecting biological functions and the development of novel drug molecules are central tasks that often require screening campaigns. However, the required architecture is cost- and time-intensive. Herein we descri...
Dose-Response Relationship, Drug Robotics Drug Evaluation, Preclinical Biological Assay Mutation Humans Antineoplastic Agents Protein Binding Kelch-Like ECH-Associated Protein 1 Education, Graduate NF-E2-Related Factor 2 Ligands Protein Kinase Inhibitors Small Molecule Libraries ErbB Receptors
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