AIMC Topic: Drug Evaluation, Preclinical

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Classification and analysis of a large collection of in vivo bioassay descriptions.

PLoS computational biology Jul 5, 2017
Testing potential drug treatments in animal disease models is a decisive step of all preclinical drug discovery programs. Yet, despite the importance of such experiments for translational medicine, there have been relatively few efforts to comprehens...
Databases, Factual Reproducibility of Results Sensitivity and Specificity Machine Learning Biological Assay Data Mining High-Throughput Screening Assays Natural Language Processing Drug Evaluation, Preclinical
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Predicting anatomic therapeutic chemical classification codes using tiered learning.

BMC bioinformatics Jun 7, 2017
BACKGROUND: The low success rate and high cost of drug discovery requires the development of new paradigms to identify molecules of therapeutic value. The Anatomical Therapeutic Chemical (ATC) Code System is a World Health Organization (WHO) proposed...
Drug Evaluation, Preclinical Machine Learning Algorithms Reproducibility of Results Drug Discovery Computational Biology
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Improving virtual screening predictive accuracy of Human kallikrein 5 inhibitors using machine learning models.

Computational biology and chemistry May 29, 2017
The readily available high throughput screening (HTS) data from the PubChem database provides an opportunity for mining of small molecules in a variety of biological systems using machine learning techniques. From the thousands of available molecular...
Machine Learning Models, Molecular Drug Evaluation, Preclinical Humans Quantitative Structure-Activity Relationship Kallikreins Small Molecule Libraries High-Throughput Screening Assays Enzyme Inhibitors
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Protein-Ligand Scoring with Convolutional Neural Networks.

Journal of chemical information and modeling Apr 11, 2017
Computational approaches to drug discovery can reduce the time and cost associated with experimental assays and enable the screening of novel chemotypes. Structure-based drug design methods rely on scoring functions to rank and predict binding affini...
Models, Molecular Drug Evaluation, Preclinical Ligands Computational Biology Proteins Protein Conformation Neural Networks, Computer User-Computer Interface
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The influence of the negative-positive ratio and screening database size on the performance of machine learning-based virtual screening.

PloS one Apr 6, 2017
The machine learning-based virtual screening of molecular databases is a commonly used approach to identify hits. However, many aspects associated with training predictive models can influence the final performance and, consequently, the number of hi...
Machine Learning Drug Evaluation, Preclinical Databases, Chemical
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FABRICATION AND EVALUATION OF SMART NANOCRYSTALS OF ARTEMISININ FOR ANTIMALARIAL AND ANTIBACTERIAL EFFICACY.

African journal of traditional, complementary, and alternative medicines : AJTCAM Nov 23, 2016
BACKGROUND: Nanocrystals have the potential to substantially increase dissolution rate, solubility with subsequent enhanced bioavailability via the oral route of a range of poor water soluble drugs. Regardless of other issues, scale up of the batch s...
Artemisinins Drug Evaluation, Preclinical Nanoparticles Shigella Antimalarials Escherichia coli Drug Stability Anti-Bacterial Agents Solubility Plasmodium falciparum Microbial Sensitivity Tests Drug Compounding
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A rapid integrated bioactivity evaluation system based on near-infrared spectroscopy for quality control of Flos Chrysanthemi.

Journal of pharmaceutical and biomedical analysis Sep 5, 2016
For quality control of herbal medicines or functional foods, integral activity evaluation has become more popular in recent studies. The majority of researchers focus on the relationship between chromatography/mass spectroscopy and bioactivity, but t...
Drug Evaluation, Preclinical Flowers Chrysanthemum Time Factors Drugs, Chinese Herbal Neural Networks, Computer Spectroscopy, Near-Infrared
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Protease Inhibitors in View of Peptide Substrate Databases.

Journal of chemical information and modeling Jun 9, 2016
Protease substrate profiling has nowadays almost become a routine task for experimentalists, and the knowledge on protease peptide substrates is easily accessible via the MEROPS database. We present a shape-based virtual screening workflow using vROC...
Machine Learning User-Computer Interface Models, Molecular Peptides Small Molecule Libraries Peptide Hydrolases Protein Conformation Databases, Pharmaceutical Substrate Specificity Drug Evaluation, Preclinical Protease Inhibitors Ligands
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Effect of Binding Pose and Modeled Structures on SVMGen and GlideScore Enrichment of Chemical Libraries.

Journal of chemical information and modeling May 24, 2016
Virtual screening consists of docking libraries of small molecules to a target protein followed by rank-ordering of the resulting structures using scoring functions. The ability of scoring methods to distinguish between actives and inactives depends ...
Small Molecule Libraries Molecular Docking Simulation Protein Kinases Support Vector Machine User-Computer Interface Ligands Drug Evaluation, Preclinical Protein Binding Crystallography, X-Ray
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Knowledge-Based Methods To Train and Optimize Virtual Screening Ensembles.

Journal of chemical information and modeling May 3, 2016
Ensemble docking can be a successful virtual screening technique that addresses the innate conformational heterogeneity of macromolecular drug targets. Yet, lacking a method to identify a subset of conformational states that effectively segregates ac...
Protein Conformation User-Computer Interface Molecular Dynamics Simulation Drug Evaluation, Preclinical Machine Learning
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