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Drug Design

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Applications of machine learning in GPCR bioactive ligand discovery.

Current opinion in structural biology Apr 18, 2019
GPCRs constitute the largest druggable family having targets for 475 Food and Drug Administration (FDA) approved drugs. As GPCRs are of great interest to pharmaceutical industry, enormous efforts are being expended to find relevant and potent GPCR li...
Drug Discovery Receptors, G-Protein-Coupled Ligands Machine Learning Drug Design Humans
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Autonomous Molecular Design: Then and Now.

ACS applied materials & interfaces Mar 25, 2019
The success of deep machine learning in processing of large amounts of data, for example, in image or voice recognition and generation, raises the possibilities that these tools can also be applied for solving complex problems in materials science. I...
Machine Learning Computer Simulation Models, Molecular Drug Design
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Automated discovery of GPCR bioactive ligands.

Current opinion in structural biology Mar 23, 2019
While G-protein-coupled receptors (GPCRs) constitute the largest class of membrane proteins, structures and endogenous ligands of a large portion of GPCRs remain unknown. Because of the involvement of GPCRs in various signaling pathways and physiolog...
Molecular Dynamics Simulation Ligands Protein Conformation Drug Discovery Receptors, G-Protein-Coupled Machine Learning Molecular Docking Simulation Drug Design Humans
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Elucidating the druggability of the human proteome with eFindSite.

Journal of computer-aided molecular design Mar 19, 2019
Identifying the viability of protein targets is one of the preliminary steps of drug discovery. Determining the ability of a protein to bind drugs in order to modulate its function, termed the druggability, requires a non-trivial amount of time and r...
Proteome Proteins Supervised Machine Learning Drug Discovery Protein Binding 5-Aminolevulinate Synthetase Serine Proteases Binding Sites Software Drug Design Humans
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GuacaMol: Benchmarking Models for de Novo Molecular Design.

Journal of chemical information and modeling Mar 19, 2019
De novo design seeks to generate molecules with required property profiles by virtual design-make-test cycles. With the emergence of deep learning and neural generative models in many application areas, models for molecular design based on neural net...
Drug Design Deep Learning Quantitative Structure-Activity Relationship Monte Carlo Method Models, Molecular Benchmarking Pharmaceutical Preparations Molecular Structure Isomerism
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From chemoinformatics to deep learning: an open road to drug discovery.

Future medicinal chemistry Mar 19, 2019
Deep Learning Databases, Pharmaceutical Neural Networks, Computer Computer Simulation Drug Design
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Discovery of small molecule binders of human FSHR(TMD) with novel structural scaffolds by integrating structural bioinformatics and machine learning algorithms.

Journal of molecular graphics & modelling Mar 5, 2019
BACKGROUND: The activation of follicle stimulating hormone receptor (FSHR) by FSH and the consequent downstream signaling activities are crucial for reproductive health. The role of FSHR in tumor progression as well as osteoporosis advancement has al...
Humans Binding Sites Computers, Molecular Models, Molecular Protein Binding Machine Learning Computational Biology Quantitative Structure-Activity Relationship Drug Design Ligands Hydrophobic and Hydrophilic Interactions Cell Line Receptors, FSH
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De Novo Molecular Design by Combining Deep Autoencoder Recurrent Neural Networks with Generative Topographic Mapping.

Journal of chemical information and modeling Mar 5, 2019
Here we show that Generative Topographic Mapping (GTM) can be used to explore the latent space of the SMILES-based autoencoders and generate focused molecular libraries of interest. We have built a sequence-to-sequence neural network with Bidirection...
Catalytic Domain Small Molecule Libraries Receptor, Adenosine A2A Drug Evaluation, Preclinical Ligands Drug Design Deep Learning Molecular Docking Simulation
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Shape-Based Generative Modeling for de Novo Drug Design.

Journal of chemical information and modeling Feb 28, 2019
In this work, we propose a machine learning approach to generate novel molecules starting from a seed compound, its three-dimensional (3D) shape, and its pharmacophoric features. The pipeline draws inspiration from generative models used in image ana...
Hydrophobic and Hydrophilic Interactions Drug Design Hydrogen Bonding Molecular Conformation Molecular Structure Machine Learning Models, Molecular Quantitative Structure-Activity Relationship Pharmaceutical Preparations
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Deep learning for molecular generation.

Future medicinal chemistry Jan 30, 2019
De novo drug design aims to generate novel chemical compounds with desirable chemical and pharmacological properties from scratch using computer-based methods. Recently, deep generative neural networks have become a very active research frontier in d...
Drug Discovery Software Humans Deep Learning Neural Networks, Computer Animals Drug Design
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