Search Results - "Koes, David"

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  1. 1

    Hidden bias in the DUD-E dataset leads to misleading performance of deep learning in structure-based virtual screening by Chen, Lieyang, Cruz, Anthony, Ramsey, Steven, Dickson, Callum J, Duca, Jose S, Hornak, Viktor, Koes, David R, Kurtzman, Tom

    Published in PloS one (20-08-2019)
    “…Recently much effort has been invested in using convolutional neural network (CNN) models trained on 3D structural images of protein-ligand complexes to…”
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  2. 2

    libmolgrid: Graphics Processing Unit Accelerated Molecular Gridding for Deep Learning Applications by Sunseri, Jocelyn, Koes, David R

    “…We describe libmolgrid, a general-purpose library for representing three-dimensional molecules using multidimensional arrays of voxelized molecular data…”
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  3. 3

    SolTranNet–A Machine Learning Tool for Fast Aqueous Solubility Prediction by Francoeur, Paul G, Koes, David R

    “…While accurate prediction of aqueous solubility remains a challenge in drug discovery, machine learning (ML) approaches have become increasingly popular for…”
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  4. 4

    DeepFrag: a deep convolutional neural network for fragment-based lead optimization by Green, Harrison, Koes, David R, Durrant, Jacob D

    Published in Chemical science (Cambridge) (08-05-2021)
    “…Machine learning has been increasingly applied to the field of computer-aided drug discovery in recent years, leading to notable advances in binding-affinity…”
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  5. 5

    GNINA 1.0: molecular docking with deep learning by McNutt, Andrew T., Francoeur, Paul, Aggarwal, Rishal, Masuda, Tomohide, Meli, Rocco, Ragoza, Matthew, Sunseri, Jocelyn, Koes, David Ryan

    Published in Journal of cheminformatics (09-06-2021)
    “…Molecular docking computationally predicts the conformation of a small molecule when binding to a receptor. Scoring functions are a vital piece of any…”
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    Three-Dimensional Convolutional Neural Networks and a Cross-Docked Data Set for Structure-Based Drug Design by Francoeur, Paul G, Masuda, Tomohide, Sunseri, Jocelyn, Jia, Andrew, Iovanisci, Richard B, Snyder, Ian, Koes, David R

    “…One of the main challenges in drug discovery is predicting protein–ligand binding affinity. Recently, machine learning approaches have made substantial…”
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  8. 8

    Pharmit: interactive exploration of chemical space by Sunseri, Jocelyn, Koes, David Ryan

    Published in Nucleic acids research (08-07-2016)
    “…Pharmit (http://pharmit.csb.pitt.edu) provides an online, interactive environment for the virtual screening of large compound databases using pharmacophores,…”
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  9. 9

    Generating 3D molecules conditional on receptor binding sites with deep generative models by Ragoza, Matthew, Masuda, Tomohide, Koes, David Ryan

    Published in Chemical science (Cambridge) (02-03-2022)
    “…The goal of structure-based drug discovery is to find small molecules that bind to a given target protein. Deep learning has been used to generate drug-like…”
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  10. 10

    Deciphering the Role of Fatty Acid-Metabolizing CYP4F11 in Lung Cancer and Its Potential As a Drug Target by Jia, Huiting, Brixius, Bjoern, Bocianoski, Caleb, Ray, Sutapa, Koes, David R, Brixius-Anderko, Simone

    Published in Drug metabolism and disposition (01-02-2024)
    “…Lung cancer is the leading cause of cancer deaths worldwide. We found that the cytochrome P450 isoform CYP4F11 is significantly overexpressed in patients with…”
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  11. 11

    Systematic Comparison of Experimental Crystallographic Geometries and Gas-Phase Computed Conformers for Torsion Preferences by Folmsbee, Dakota L., Koes, David R., Hutchison, Geoffrey R.

    “…We performed exhaustive torsion sampling on more than 3 million compounds using the GFN2-xTB method and performed a comparison of experimental crystallographic…”
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  12. 12

    AnchorQuery: Rapid online virtual screening for small‐molecule protein–protein interaction inhibitors by Koes, David R., Dömling, Alexander, Camacho, Carlos J.

    Published in Protein science (01-01-2018)
    “…AnchorQuery (http://anchorquery.csb.pitt.edu) is a web application for rational structure‐based design of protein–protein interaction (PPI) inhibitors. A…”
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  13. 13

    Evaluation of Thermochemical Machine Learning for Potential Energy Curves and Geometry Optimization by Folmsbee, Dakota L, Koes, David R, Hutchison, Geoffrey R

    “…While many machine learning (ML) methods, particularly deep neural networks, have been trained for density functional and quantum chemical energies and…”
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    Virtual Screening with Gnina 1.0 by Sunseri, Jocelyn, Koes, David Ryan

    Published in Molecules (Basel, Switzerland) (04-12-2021)
    “…Virtual screening-predicting which compounds within a specified compound library bind to a target molecule, typically a protein-is a fundamental task in the…”
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  16. 16

    SidechainNet: An all‐atom protein structure dataset for machine learning by King, Jonathan Edward, Koes, David Ryan

    “…Despite recent advancements in deep learning methods for protein structure prediction and representation, little focus has been directed at the simultaneous…”
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  17. 17

    Evaluating amber force fields using computed NMR chemical shifts by Koes, David R., Vries, John K.

    “…NMR chemical shifts can be computed from molecular dynamics (MD) simulations using a template matching approach and a library of conformers containing chemical…”
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  18. 18

    Expanding Training Data for Structure-Based Receptor–Ligand Binding Affinity Regression through Imputation of Missing Labels by Francoeur, Paul G., Koes, David R.

    Published in ACS omega (07-11-2023)
    “…The success of machine learning is, in part, due to a large volume of data available to train models. However, the amount of training data for structure-based…”
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  19. 19

    Threonine 89 Is an Important Residue of Profilin-1 That Is Phosphorylatable by Protein Kinase A by Gau, David, Veon, William, Zeng, Xuemei, Yates, Nathan, Shroff, Sanjeev G, Koes, David R, Roy, Partha

    Published in PloS one (26-05-2016)
    “…Dynamic regulation of actin cytoskeleton is at the heart of all actin-based cellular events. In this study, we sought to identify novel post-translational…”
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  20. 20

    Enabling large-scale design, synthesis and validation of small molecule protein-protein antagonists by Koes, David, Khoury, Kareem, Huang, Yijun, Wang, Wei, Bista, Michal, Popowicz, Grzegorz M, Wolf, Siglinde, Holak, Tad A, Dömling, Alexander, Camacho, Carlos J

    Published in PloS one (12-03-2012)
    “…Although there is no shortage of potential drug targets, there are only a handful known low-molecular-weight inhibitors of protein-protein interactions (PPIs)…”
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