AIMC Topic: Drug Design

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Designing Anticancer Peptides by Constructive Machine Learning.

ChemMedChem May 29, 2018
Constructive (generative) machine learning enables the automated generation of novel chemical structures without the need for explicit molecular design rules. This study presents the experimental application of such a deep machine learning model to d...
Humans Deep Learning MCF-7 Cells Amino Acid Sequence Peptides Drug Design Antineoplastic Agents
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Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era.

The AAPS journal Mar 30, 2018
Over the last decade, deep learning (DL) methods have been extremely successful and widely used to develop artificial intelligence (AI) in almost every domain, especially after it achieved its proud record on computational Go. Compared to traditional...
Artificial Intelligence Drug Design Drug Discovery Deep Learning Big Data
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Prediction of pKa Values for Neutral and Basic Drugs based on Hybrid Artificial Intelligence Methods.

Scientific reports Mar 5, 2018
The pKa value of drugs is an important parameter in drug design and pharmacology. In this paper, an improved particle swarm optimization (PSO) algorithm was proposed based on the population entropy diversity. In the improved algorithm, when the popul...
Pharmaceutical Preparations Entropy Artificial Intelligence Algorithms Drug Design Neural Networks, Computer
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Deep Generative Models for Molecular Science.

Molecular informatics Feb 6, 2018
Generative deep machine learning models now rival traditional quantum-mechanical computations in predicting properties of new structures, and they come with a significantly lower computational cost, opening new avenues in computational molecular scie...
Deep Learning Models, Chemical Quantitative Structure-Activity Relationship Drug Design
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Boosted neural networks scoring functions for accurate ligand docking and ranking.

Journal of bioinformatics and computational biology Feb 4, 2018
Predicting the native poses of ligands correctly is one of the most important steps towards successful structure-based drug design. Binding affinities (BAs) estimated by traditional scoring functions (SFs) are typically used to score and rank-order p...
Molecular Docking Simulation Drug Design Protein Binding Neural Networks, Computer Machine Learning Algorithms Ligands Escherichia coli Proteins tRNA Methyltransferases Proteins Databases, Protein Structure-Activity Relationship
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De Novo Design of Bioactive Small Molecules by Artificial Intelligence.

Molecular informatics Jan 10, 2018
Generative artificial intelligence offers a fresh view on molecular design. We present the first-time prospective application of a deep learning model for designing new druglike compounds with desired activities. For this purpose, we trained a recurr...
Peroxisome Proliferator-Activated Receptors Small Molecule Libraries Retinoid X Receptors Deep Learning HEK293 Cells Quantitative Structure-Activity Relationship Humans Molecular Docking Simulation Drug Design
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Design, synthesis and biological evaluation of 7H-pyrrolo[2,3-d]pyrimidin-4-amine derivatives as selective Btk inhibitors with improved pharmacokinetic properties for the treatment of rheumatoid arthritis.

European journal of medicinal chemistry Dec 28, 2017
Bruton's tyrosine kinase (Btk) is a Tec family kinase with a well-defined role in the B cell receptor (BCR) and Fcγ receptor (FcR) signaling pathways, which makes it a uniquely attractive target for the treatment of autoimmune diseases, such as rheum...
Dogs Microsomes, Liver Protein Kinase Inhibitors Rats Structure-Activity Relationship Pyrimidines Cell Proliferation Models, Molecular Pyrroles Agammaglobulinaemia Tyrosine Kinase Rats, Sprague-Dawley Drug Design Molecular Structure Dose-Response Relationship, Drug Haplorhini Humans Mice Cell Line Arthritis, Rheumatoid Animals Protein-Tyrosine Kinases
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Application of Generative Autoencoder in De Novo Molecular Design.

Molecular informatics Dec 13, 2017
A major challenge in computational chemistry is the generation of novel molecular structures with desirable pharmacological and physiochemical properties. In this work, we investigate the potential use of autoencoder, a deep learning methodology, for...
Deep Learning Quantitative Structure-Activity Relationship Drug Design
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Design, synthesis, antibacterial evaluation and molecular docking studies of some new quinoxaline derivatives targeting dihyropteroate synthase enzyme.

Bioorganic chemistry Dec 5, 2017
Development of new antimicrobial agents is a good solution to overcome drug-resistance problems. From this perspective, new quinoxaline derivatives bearing various bioactive heterocyclic moieties (thiadiazoles, oxadiazoles, pyrazoles and thiazoles) w...
Catalytic Domain Gram-Negative Bacteria Gram-Positive Bacteria Hydrogen Bonding Dihydropteroate Synthase Quinoxalines Structure-Activity Relationship Molecular Structure Hydrophobic and Hydrophilic Interactions Anti-Bacterial Agents Drug Design Molecular Docking Simulation Microbial Sensitivity Tests Levofloxacin Protein Binding Yersinia pestis
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Modern drug design: the implication of using artificial neuronal networks and multiple molecular dynamic simulations.

Journal of computer-aided molecular design Nov 13, 2017
We report the implementation of molecular modeling approaches developed as a part of the 2016 Grand Challenge 2, the blinded competition of computer aided drug design technologies held by the D3R Drug Design Data Resource ( https://drugdesigndata.org...
Computer-Aided Design Receptors, Cytoplasmic and Nuclear Drug Design Protein Binding Binding Sites Neural Networks, Computer Molecular Dynamics Simulation Humans Machine Learning Ligands Molecular Docking Simulation Protein Conformation
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