AIMC Topic: Drug Design

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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...
Drug Design Drug Discovery Protein Binding Humans Software 5-Aminolevulinate Synthetase Supervised Machine Learning Serine Proteases Binding Sites Proteome Proteins
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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...
Monte Carlo Method Benchmarking Isomerism Quantitative Structure-Activity Relationship Drug Design Models, Molecular Deep Learning Molecular Structure Pharmaceutical Preparations
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From chemoinformatics to deep learning: an open road to drug discovery.

Future medicinal chemistry Mar 19, 2019
Computer Simulation Drug Design Deep Learning Neural Networks, Computer Databases, Pharmaceutical
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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...
Binding Sites Computers, Molecular Cell Line Models, Molecular Ligands Hydrophobic and Hydrophilic Interactions Quantitative Structure-Activity Relationship Receptors, FSH Drug Design Protein Binding Computational Biology Humans Machine Learning
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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...
Molecular Docking Simulation Drug Design Catalytic Domain Small Molecule Libraries Receptor, Adenosine A2A Deep Learning Drug Evaluation, Preclinical Ligands
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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...
Molecular Structure Pharmaceutical Preparations Machine Learning Molecular Conformation Drug Design Quantitative Structure-Activity Relationship Hydrophobic and Hydrophilic Interactions Models, Molecular Hydrogen Bonding
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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 Neural Networks, Computer Deep Learning Animals Drug Design
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Predictive Multitask Deep Neural Network Models for ADME-Tox Properties: Learning from Large Data Sets.

Journal of chemical information and modeling Jan 24, 2019
Successful drug discovery projects require control and optimization of compound properties related to pharmacokinetics, pharmacodynamics, and safety. While volume and chemotype coverage of public and corporate ADME-Tox (absorption, distribution, excr...
Drug Design Models, Statistical Humans Caco-2 Cells Permeability Models, Molecular Neural Networks, Computer Quantitative Structure-Activity Relationship Pharmaceutical Preparations Databases, Pharmaceutical Models, Biological
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Deep Learning: Current and Emerging Applications in Medicine and Technology.

IEEE journal of biomedical and health informatics Jan 23, 2019
Machine learning is enabling researchers to analyze and understand increasingly complex physical and biological phenomena in traditional fields such as biology, medicine, and engineering and emerging fields like synthetic biology, automated chemical ...
Nanomedicine Humans Image Interpretation, Computer-Assisted Drug Design Computational Biology Drug Discovery Biotechnology Robotics Deep Learning
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Network-Based Assessment of Adverse Drug Reaction Risk in Polypharmacy Using High-Throughput Screening Data.

International journal of molecular sciences Jan 17, 2019
The risk of adverse drug reactions increases in a polypharmacology setting. High-throughput drug screening with transcriptomics applied to human cells has shown that drugs have effects on several molecular pathways, and these affected pathways may be...
Polypharmacy Risk Assessment Drug-Related Side Effects and Adverse Reactions Drug Design High-Throughput Screening Assays Data Interpretation, Statistical Humans Neural Networks, Computer Algorithms
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