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  1. Artikel ; Online: Compound Ranking Based on Fuzzy Three-Dimensional Similarity Improves the Performance of Docking into Homology Models of G‑Protein-Coupled Receptors

    Andrew Anighoro / Jürgen Bajorath

    ACS Omega, Vol 2, Iss 6, Pp 2583-

    2017  Band 2592

    Schlagwörter Chemistry ; QD1-999
    Sprache Englisch
    Erscheinungsdatum 2017-06-01T00:00:00Z
    Verlag American Chemical Society
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  2. Artikel ; Online: Learning protein-ligand binding affinity with atomic environment vectors

    Rocco Meli / Andrew Anighoro / Mike J. Bodkin / Garrett M. Morris / Philip C. Biggin

    Journal of Cheminformatics, Vol 13, Iss 1, Pp 1-

    2021  Band 19

    Abstract: Abstract Scoring functions for the prediction of protein-ligand binding affinity have seen renewed interest in recent years when novel machine learning and deep learning methods started to consistently outperform classical scoring functions. Here we ... ...

    Abstract Abstract Scoring functions for the prediction of protein-ligand binding affinity have seen renewed interest in recent years when novel machine learning and deep learning methods started to consistently outperform classical scoring functions. Here we explore the use of atomic environment vectors (AEVs) and feed-forward neural networks, the building blocks of several neural network potentials, for the prediction of protein-ligand binding affinity. The AEV-based scoring function, which we term AEScore, is shown to perform as well or better than other state-of-the-art scoring functions on binding affinity prediction, with an RMSE of 1.22 pK units and a Pearson’s correlation coefficient of 0.83 for the CASF-2016 benchmark. However, AEScore does not perform as well in docking and virtual screening tasks, for which it has not been explicitly trained. Therefore, we show that the model can be combined with the classical scoring function AutoDock Vina in the context of $$\Delta$$ Δ -learning, where corrections to the AutoDock Vina scoring function are learned instead of the protein-ligand binding affinity itself. Combined with AutoDock Vina, $$\Delta$$ Δ -AEScore has an RMSE of 1.32 pK units and a Pearson’s correlation coefficient of 0.80 on the CASF-2016 benchmark, while retaining the docking and screening power of the underlying classical scoring function.
    Schlagwörter Binding affinity ; Scoring function ; Deep learning ; Information technology ; T58.5-58.64 ; Chemistry ; QD1-999
    Thema/Rubrik (Code) 006
    Sprache Englisch
    Erscheinungsdatum 2021-08-01T00:00:00Z
    Verlag BMC
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  3. Artikel ; Online: A human-based multi-gene signature enables quantitative drug repurposing for metabolic disease

    James A Timmons / Andrew Anighoro / Robert J Brogan / Jack Stahl / Claes Wahlestedt / David Gordon Farquhar / Jake Taylor-King / Claude-Henry Volmar / William E Kraus / Stuart M Phillips

    eLife, Vol

    2022  Band 11

    Abstract: Insulin resistance (IR) contributes to the pathophysiology of diabetes, dementia, viral infection, and cardiovascular disease. Drug repurposing (DR) may identify treatments for IR; however, barriers include uncertainty whether in vitro transcriptomic ... ...

    Abstract Insulin resistance (IR) contributes to the pathophysiology of diabetes, dementia, viral infection, and cardiovascular disease. Drug repurposing (DR) may identify treatments for IR; however, barriers include uncertainty whether in vitro transcriptomic assays yield quantitative pharmacological data, or how to optimise assay design to best reflect in vivo human disease. We developed a clinical-based human tissue IR signature by combining lifestyle-mediated treatment responses (>500 human adipose and muscle biopsies) with biomarkers of disease status (fasting IR from >1200 biopsies). The assay identified a chemically diverse set of >130 positively acting compounds, highly enriched in true positives, that targeted 73 proteins regulating IR pathways. Our multi-gene RNA assay score reflected the quantitative pharmacological properties of a set of epidermal growth factor receptor-related tyrosine kinase inhibitors, providing insight into drug target specificity; an observation supported by deep learning-based genome-wide predicted pharmacology. Several drugs identified are suitable for evaluation in patients, particularly those with either acute or severe chronic IR.
    Schlagwörter drug repurposing ; insulin biology ; deep learning ; Diabetes ; Transcriptomics ; Exercise ; Medicine ; R ; Science ; Q ; Biology (General) ; QH301-705.5
    Sprache Englisch
    Erscheinungsdatum 2022-01-01T00:00:00Z
    Verlag eLife Sciences Publications Ltd
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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