AIMC Topic: Drug Discovery

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Predicting Drug-Target Interactions via Within-Score and Between-Score.

BioMed research international Oct 12, 2015
Network inference and local classification models have been shown to be useful in predicting newly potential drug-target interactions (DTIs) for assisting in drug discovery or drug repositioning. The idea is to represent drugs, targets, and their int...
Drug Discovery Models, Theoretical Drug Repositioning Reproducibility of Results Humans Drug Delivery Systems Artificial Intelligence Algorithms Principal Component Analysis Data Interpretation, Statistical Drug Industry
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Predicting Mouse Liver Microsomal Stability with "Pruned" Machine Learning Models and Public Data.

Pharmaceutical research Sep 28, 2015
PURPOSE: Mouse efficacy studies are a critical hurdle to advance translational research of potential therapeutic compounds for many diseases. Although mouse liver microsomal (MLM) stability studies are not a perfect surrogate for in vivo studies of m...
Bayes Theorem Pharmaceutical Preparations Machine Learning Models, Biological Drug Discovery Microsomes, Liver Animals Mice Small Molecule Libraries Principal Component Analysis Databases, Pharmaceutical
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Computational probing protein-protein interactions targeting small molecules.

Bioinformatics (Oxford, England) Sep 28, 2015
MOTIVATION: With the booming of interactome studies, a lot of interactions can be measured in a high throughput way and large scale datasets are available. It is becoming apparent that many different types of interactions can be potential drug target...
Humans Algorithms Pharmaceutical Preparations Protein Interaction Mapping Sequence Analysis, Protein Drug Discovery Support Vector Machine Proteins
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Connecting proteins with drug-like compounds: Open source drug discovery workflows with BindingDB and KNIME.

Database : the journal of biological databases and curation Sep 16, 2015
Today's large, public databases of protein-small molecule interaction data are creating important new opportunities for data mining and integration. At the same time, new graphical user interface-based workflow tools offer facile alternatives to cust...
Pharmacokinetics Humans Pharmaceutical Preparations Animals Protein Binding Drug Interactions Drug Discovery Proteins Knowledge Bases
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Neural-Network Scoring Functions Identify Structurally Novel Estrogen-Receptor Ligands.

Journal of chemical information and modeling Sep 4, 2015
The magnitude of the investment required to bring a drug to the market hinders medical progress, requiring hundreds of millions of dollars and years of research and development. Any innovation that improves the efficiency of the drug-discovery proces...
Humans Databases, Factual Protein Binding Ligands Neural Networks, Computer Estradiol Models, Biological Molecular Conformation Receptors, Estrogen Computer Simulation Drug Discovery
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Inferring Association between Compound and Pathway with an Improved Ensemble Learning Method.

Molecular informatics Aug 6, 2015
Emergence of compound molecular data coupled to pathway information offers the possibility of using machine learning methods for compound-pathway associations' inference. To provide insights into the global relationship between compounds and their af...
MCF-7 Cells Drug Discovery Molecular Structure Antineoplastic Agents Oligonucleotide Array Sequence Analysis Humans Databases, Genetic Algorithms
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Machine Learning Models and Pathway Genome Data Base for Trypanosoma cruzi Drug Discovery.

PLoS neglected tropical diseases Jun 26, 2015
BACKGROUND: Chagas disease is a neglected tropical disease (NTD) caused by the eukaryotic parasite Trypanosoma cruzi. The current clinical and preclinical pipeline for T. cruzi is extremely sparse and lacks drug target diversity.
Humans Machine Learning Female Animals Trypanocidal Agents Drug Discovery Metabolic Networks and Pathways Disease Models, Animal Mice Genome, Protozoan High-Throughput Screening Assays Mice, Inbred BALB C Computational Biology Trypanosoma cruzi Bayes Theorem Cell Line Chagas Disease
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Comparing the Influence of Simulated Experimental Errors on 12 Machine Learning Algorithms in Bioactivity Modeling Using 12 Diverse Data Sets.

Journal of chemical information and modeling Jun 18, 2015
To date, no systematic study has assessed the effect of random experimental errors on the predictive power of QSAR models. To address this shortage, we have benchmarked the noise sensitivity of 12 learning algorithms on 12 data sets (15,840 models in...
Quantitative Structure-Activity Relationship Drug Discovery Models, Statistical Machine Learning Algorithms
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The role of machine learning in neuroimaging for drug discovery and development.

Psychopharmacology May 28, 2015
Neuroimaging has been identified as a potentially powerful probe for the in vivo study of drug effects on the brain with utility across several phases of drug development spanning preclinical and clinical investigations. Specifically, neuroimaging ca...
Neuroimaging Machine Learning Brain Drug Discovery Humans
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Efficient heuristics for maximum common substructure search.

Journal of chemical information and modeling May 1, 2015
Maximum common substructure search is a computationally hard optimization problem with diverse applications in the field of cheminformatics, including similarity search, lead optimization, molecule alignment, and clustering. Most of these application...
Drug Discovery Time Factors Computer Heuristics
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