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Small Molecule Libraries

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Creating the New from the Old: Combinatorial Libraries Generation with Machine-Learning-Based Compound Structure Optimization.

Journal of chemical information and modeling Feb 15, 2017
The growing computational abilities of various tools that are applied in the broadly understood field of computer-aided drug design have led to the extreme popularity of virtual screening in the search for new biologically active compounds. Most ofte...
Databases, Pharmaceutical Combinatorial Chemistry Techniques Machine Learning Computer-Aided Design Drug Design Small Molecule Libraries
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Predicting DPP-IV inhibitors with machine learning approaches.

Journal of computer-aided molecular design Feb 2, 2017
Dipeptidyl peptidase IV (DPP-IV) is a promising Type 2 diabetes mellitus (T2DM) drug target. DPP-IV inhibitors prolong the action of glucagon-like peptide-1 (GLP-1) and gastric inhibitory peptide (GIP), improve glucose homeostasis without weight gain...
Machine Learning Molecular Docking Simulation Computer-Aided Design Humans Dipeptidyl Peptidase 4 Small Molecule Libraries Dipeptidyl-Peptidase IV Inhibitors Drug Design Diabetes Mellitus, Type 2 Bayes Theorem
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Performance of Deep and Shallow Neural Networks, the Universal Approximation Theorem, Activity Cliffs, and QSAR.

Molecular informatics Oct 26, 2016
Neural networks have generated valuable Quantitative Structure-Activity/Property Relationships (QSAR/QSPR) models for a wide variety of small molecules and materials properties. They have grown in sophistication and many of their initial problems hav...
Quantitative Structure-Activity Relationship Small Molecule Libraries Machine Learning Molecular Docking Simulation Drug Design
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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...
Peptides User-Computer Interface Models, Molecular Machine Learning Substrate Specificity Protease Inhibitors Small Molecule Libraries Drug Evaluation, Preclinical Ligands Protein Conformation Peptide Hydrolases Databases, Pharmaceutical
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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 ...
Crystallography, X-Ray Small Molecule Libraries User-Computer Interface Protein Binding Support Vector Machine Drug Evaluation, Preclinical Protein Kinases Ligands Molecular Docking Simulation
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Fully automatized high-throughput enzyme library screening using a robotic platform.

Biotechnology and bioengineering Feb 3, 2016
A fully automatized robotic platform has been established to facilitate high-throughput screening for protein engineering purposes. This platform enables proper monitoring and control of growth conditions in the microtiter plate format to ensure prec...
Small Molecule Libraries Transaminases Enzyme Assays Protein Engineering Automation, Laboratory Robotics Equipment Design High-Throughput Screening Assays
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Highly predictive support vector machine (SVM) models for anthrax toxin lethal factor (LF) inhibitors.

Journal of molecular graphics & modelling Nov 17, 2015
Anthrax is a highly lethal, acute infectious disease caused by the rod-shaped, Gram-positive bacterium Bacillus anthracis. The anthrax toxin lethal factor (LF), a zinc metalloprotease secreted by the bacilli, plays a key role in anthrax pathogenesis ...
User-Computer Interface Support Vector Machine Models, Statistical Predictive Value of Tests Structure-Activity Relationship Antidotes Bacillus anthracis Bacterial Toxins Models, Chemical Antigens, Bacterial High-Throughput Screening Assays Small Molecule Libraries Databases, Chemical
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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...
Databases, Pharmaceutical Models, Biological Microsomes, Liver Small Molecule Libraries Bayes Theorem Pharmaceutical Preparations Drug Discovery Machine Learning Mice Animals Principal Component Analysis
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BiNChE: a web tool and library for chemical enrichment analysis based on the ChEBI ontology.

BMC bioinformatics Feb 21, 2015
BACKGROUND: Ontology-based enrichment analysis aids in the interpretation and understanding of large-scale biological data. Ontologies are hierarchies of biologically relevant groupings. Using ontology annotations, which link ontology classes to biol...
Small Molecule Libraries Pharmaceutical Preparations Biological Ontologies Databases, Chemical Software Internet
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Bayesian models trained with HTS data for predicting β-haematin inhibition and in vitro antimalarial activity.

Bioorganic & medicinal chemistry Dec 20, 2014
A large quantity of high throughput screening (HTS) data for antimalarial activity has become available in recent years. This includes both phenotypic and target-based activity. Realising the maximum value of these data remains a challenge. In this r...
Parasitic Sensitivity Tests Small Molecule Libraries Databases, Pharmaceutical Models, Biological Antimalarials Malaria, Falciparum Hemeproteins Humans Machine Learning Drug Discovery Plasmodium falciparum Bayes Theorem
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