AIMC Topic: Models, Molecular

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Predicting Drug-Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation.

Journal of chemical information and modeling Sep 6, 2019
We propose a novel deep learning approach for predicting drug-target interaction using a graph neural network. We introduce a distance-aware graph attention algorithm to differentiate various types of intermolecular interactions. Furthermore, we extr...
Neural Networks, Computer Algorithms Computational Biology Proteins Models, Molecular Ligands Molecular Targeted Therapy Protein Conformation Computer Graphics
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Protein model accuracy estimation based on local structure quality assessment using 3D convolutional neural network.

PloS one Sep 5, 2019
In protein tertiary structure prediction, model quality assessment programs (MQAPs) are often used to select the final structural models from a pool of candidate models generated by multiple templates and prediction methods. The 3-dimensional convolu...
Algorithms Models, Molecular Protein Structure, Tertiary Computational Biology Caspase 12 Humans Databases, Protein Neural Networks, Computer
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Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints.

Nature communications Sep 4, 2019
The inapplicability of amino acid covariation methods to small protein families has limited their use for structural annotation of whole genomes. Recently, deep learning has shown promise in allowing accurate residue-residue contact prediction even f...
Binding Sites Algorithms Reproducibility of Results Animals Humans Deep Learning Computational Biology Proteomics Proteome Models, Molecular Protein Conformation
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Deep learning enables rapid identification of potent DDR1 kinase inhibitors.

Nature biotechnology Sep 2, 2019
We have developed a deep generative model, generative tensorial reinforcement learning (GENTRL), for de novo small-molecule design. GENTRL optimizes synthetic feasibility, novelty, and biological activity. We used GENTRL to discover potent inhibitors...
Enzyme Inhibitors Protein Conformation Discoidin Domain Receptor 1 Rats Models, Molecular Molecular Structure Dogs Drug Evaluation, Preclinical Microsomes, Liver Animals Deep Learning Mice Humans
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Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism.

Journal of medicinal chemistry Aug 27, 2019
Hunting for chemicals with favorable pharmacological, toxicological, and pharmacokinetic properties remains a formidable challenge for drug discovery. Deep learning provides us with powerful tools to build predictive models that are appropriate for t...
Organic Chemicals Models, Molecular Solubility Datasets as Topic Databases, Chemical Proof of Concept Study Drug Discovery Deep Learning
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Enhanced Deep-Learning Prediction of Molecular Properties via Augmentation of Bond Topology.

ChemMedChem Aug 23, 2019
Deep learning has made great strides in tackling chemical problems, but still lacks full-fledged representations for three-dimensional (3D) molecular structures for its inner working. For example, the molecular graph, commonly used in chemistry and r...
Deep Learning Models, Molecular Ligands Datasets as Topic Molecular Conformation
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Boosting phosphorylation site prediction with sequence feature-based machine learning.

Proteins Aug 22, 2019
Protein phosphorylation is one of the essential posttranslation modifications playing a vital role in the regulation of many fundamental cellular processes. We propose a LightGBM-based computational approach that uses evolutionary, geometric, sequenc...
Protein Processing, Post-Translational Proteins Databases, Protein Reproducibility of Results Animals Threonine Computational Biology Serine Species Specificity Sequence Analysis, Protein Tyrosine Binding Sites Phosphorylation Models, Molecular Protein Conformation Humans Machine Learning Algorithms
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Reaction-Based Enumeration, Active Learning, and Free Energy Calculations To Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin-Dependent Kinase 2 Inhibitors.

Journal of chemical information and modeling Aug 22, 2019
The hit-to-lead and lead optimization processes usually involve the design, synthesis, and profiling of thousands of analogs prior to clinical candidate nomination. A hit finding campaign may begin with a virtual screen that explores millions of comp...
Thermodynamics Small Molecule Libraries Drug Discovery Cyclin-Dependent Kinase 2 Humans Models, Molecular Protein Kinase Inhibitors Machine Learning Drug Design
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Distance-based protein folding powered by deep learning.

Proceedings of the National Academy of Sciences of the United States of America Aug 9, 2019
Direct coupling analysis (DCA) for protein folding has made very good progress, but it is not effective for proteins that lack many sequence homologs, even coupled with time-consuming conformation sampling with fragments. We show that we can accurate...
Algorithms Deep Learning Time Factors Protein Folding Sequence Alignment Membrane Proteins Models, Molecular
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A Scalable Molecular Force Field Parameterization Method Based on Density Functional Theory and Quantum-Level Machine Learning.

Journal of chemical information and modeling Aug 8, 2019
Fast and accurate molecular force field (FF) parameterization is still an unsolved problem. Accurate FF are not generally available for all molecules, like novel druglike molecules. While methods based on quantum mechanics (QM) exist to parameterize ...
Neural Networks, Computer Density Functional Theory Models, Molecular Molecular Conformation
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