AIMC Topic: Drug Design

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ROCS-derived features for virtual screening.

Journal of computer-aided molecular design Sep 8, 2016
Rapid overlay of chemical structures (ROCS) is a standard tool for the calculation of 3D shape and chemical ("color") similarity. ROCS uses unweighted sums to combine many aspects of similarity, yielding parameter-free models for virtual screening. I...
Drug Design Models, Molecular Molecular Structure Pharmaceutical Preparations Machine Learning Algorithms Computer-Aided Design
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Molecular graph convolutions: moving beyond fingerprints.

Journal of computer-aided molecular design Aug 24, 2016
Molecular "fingerprints" encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications. However, fingerprint representations necessarily emphasize particular aspects of the molecular structur...
Molecular Structure Pharmaceutical Preparations Ligands Computer Graphics Neural Networks, Computer Machine Learning Computer-Aided Design Drug Design
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A renaissance of neural networks in drug discovery.

Expert opinion on drug discovery Jul 4, 2016
INTRODUCTION: Neural networks are becoming a very popular method for solving machine learning and artificial intelligence problems. The variety of neural network types and their application to drug discovery requires expert knowledge to choose the mo...
Drug Delivery Systems Computer-Aided Design Drug Design Drug Discovery Humans Structure-Activity Relationship Neural Networks, Computer Pharmaceutical Preparations Models, Biological
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Have artificial neural networks met expectations in drug discovery as implemented in QSAR framework?

Expert opinion on drug discovery May 30, 2016
INTRODUCTION: Artificial neural networks (ANNs) are highly adaptive nonlinear optimization algorithms that have been applied in many diverse scientific endeavors, ranging from economics, engineering, physics, and chemistry to medical science. Notably...
Neural Networks, Computer Algorithms Animals Drug Design Humans Nonlinear Dynamics Quantitative Structure-Activity Relationship Drug Discovery Models, Theoretical Pharmaceutical Preparations
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Machine learning optimization of cross docking accuracy.

Computational biology and chemistry May 4, 2016
Performance of small molecule automated docking programs has conceptually been divided into docking -, scoring -, ranking - and screening power, which focuses on the crystal pose prediction, affinity prediction, ligand ranking and database screening ...
Machine Learning Molecular Docking Simulation Drug Design Ligands
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Predicting drug target interactions using meta-path-based semantic network analysis.

BMC bioinformatics Apr 12, 2016
BACKGROUND: In the context of drug discovery, drug target interactions (DTIs) can be predicted based on observed topological features of a semantic network across the chemical and biological space. In a semantic network, the types of the nodes and li...
Data Mining Pharmacological Phenomena Proteins Models, Chemical Pharmaceutical Preparations Machine Learning Databases, Factual Drug Design
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The Virtual Screening of the Drug Protein with a Few Crystal Structures Based on the Adaboost-SVM.

Computational and mathematical methods in medicine Apr 3, 2016
Using the theory of machine learning to assist the virtual screening (VS) has been an effective plan. However, the quality of the training set may reduce because of mixing with the wrong docking poses and it will affect the screening efficiencies. To...
Hydrogen Cathepsin K Drug Design Ligands Binding Sites Crystallization ROC Curve Combinatorial Chemistry Techniques Protein Conformation Proteins Imaging, Three-Dimensional Chemistry, Pharmaceutical Protein Binding Databases, Protein Computational Biology Humans Area Under Curve Support Vector Machine Software Models, Statistical Algorithms Reproducibility of Results
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Antileishmanial activity of novel indolyl-coumarin hybrids: Design, synthesis, biological evaluation, molecular docking study and in silico ADME prediction.

Bioorganic & medicinal chemistry letters Dec 24, 2015
In present work we have designed and synthesized total twelve novel 3-(3-(1H-indol-3-yl)-3-phenylpropanoyl)-4-hydroxy-2H-chromen-2-one derivatives 13(a-l) using Ho(3+) doped CoFe2O4 nanoparticles as catalyst and evaluated for their potential antileis...
Oxidoreductases Humans Protein Structure, Tertiary Molecular Docking Simulation Inhibitory Concentration 50 Drug Design Coumarins Antioxidants Enzyme Inhibitors Binding Sites Cell Survival Antiprotozoal Agents Leishmania Structure-Activity Relationship Protozoan Proteins HeLa Cells
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Cenderitide: structural requirements for the creation of a novel dual particulate guanylyl cyclase receptor agonist with renal-enhancing in vivo and ex vivo actions.

European heart journal. Cardiovascular pharmacotherapy Dec 10, 2015
AIMS: Cenderitide is a novel dual natriuretic peptide (NP) receptor chimeric peptide activator, which targets the particulate guanylyl cyclase B (pGC-B) receptor and pGC-A unlike native NPs. Cenderitide was engineered to retain the anti-fibrotic prop...
Receptors, Atrial Natriuretic Factor Renal Agents Dogs Drug Design Dendroaspis Humans Snake Venoms HEK293 Cells Natriuretic Agents Glomerular Filtration Rate Animals Cyclic GMP Kidney Function Tests Natriuretic Peptide, C-Type Male Structure-Activity Relationship Natriuretic Peptides
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A Genetic Algorithm Based Support Vector Machine Model for Blood-Brain Barrier Penetration Prediction.

BioMed research international Oct 4, 2015
Blood-brain barrier (BBB) is a highly complex physical barrier determining what substances are allowed to enter the brain. Support vector machine (SVM) is a kernel-based machine learning method that is widely used in QSAR study. For a successful SVM ...
Databases, Factual Blood-Brain Barrier Drug Design Humans Support Vector Machine Quantitative Structure-Activity Relationship Algorithms Models, Biological Hydrogen Bonding Hydrogen-Ion Concentration Biological Transport, Active Permeability
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