Selected article for: "absorption distribution metabolism and activity relationship"

Author: PÅ‚onka, Wojciech; Paneth, Agata; Paneth, Piotr
Title: Docking and QSAR of Aminothioureas at the SARS-CoV-2 S-Protein–Human ACE2 Receptor Interface
  • Cord-id: figkxnnk
  • Document date: 2020_10_12
  • ID: figkxnnk
    Snippet: Docking of over 160 aminothiourea derivatives at the SARS-CoV-2 S-protein–human ACE2 receptor interface, whose structure became available recently, has been evaluated for its complex stabilizing potency and subsequently subjected to quantitative structure–activity relationship (QSAR) analysis. The structural variety of the studied compounds, that include 3 different forms of the N–N–C(S)–N skeleton and combinations of 13 different substituents alongside the extensive length of the inte
    Document: Docking of over 160 aminothiourea derivatives at the SARS-CoV-2 S-protein–human ACE2 receptor interface, whose structure became available recently, has been evaluated for its complex stabilizing potency and subsequently subjected to quantitative structure–activity relationship (QSAR) analysis. The structural variety of the studied compounds, that include 3 different forms of the N–N–C(S)–N skeleton and combinations of 13 different substituents alongside the extensive length of the interface, resulted in the failure of the QSAR analysis, since different molecules were binding to different parts of the interface. Subsequently, absorption, distribution, metabolism, and excretion (ADME) analysis on all studied compounds, followed by a toxicity analysis using statistical models for selected compounds, was carried out to evaluate their potential use as lead compounds for drug design. Combined, these studies highlighted two molecules among the studied compounds, i.e., 5-(pyrrol-2-yl)-2-(2-methoxyphenylamino)-1,3,4-thiadiazole and 1-(cyclopentanoyl)-4-(3-iodophenyl)-thiosemicarbazide, as the best candidates for the development of future drugs.

    Search related documents:
    Co phrase search for related documents
    • ace receptor and activity important: 1, 2, 3, 4
    • ace receptor and machine learning: 1, 2, 3
    • active site and adme analysis: 1, 2, 3, 4
    • active site and adme excretion: 1, 2, 3
    • active site and adme excretion metabolism: 1, 2, 3
    • active site and adme excretion metabolism distribution: 1, 2, 3
    • active site and adme excretion metabolism distribution absorption: 1, 2, 3
    • active site and adme property: 1
    • active site and admet study: 1
    • active site and low toxicity: 1, 2
    • active site and machine learning: 1, 2, 3, 4, 5, 6, 7, 8, 9
    • activity important and low toxicity: 1, 2, 3, 4
    • activity important and machine learning: 1, 2, 3
    • adme analysis and machine learning: 1
    • adme excretion and low toxicity: 1
    • adme excretion and machine learning: 1
    • adme excretion metabolism and low toxicity: 1
    • adme excretion metabolism and machine learning: 1
    • adme excretion metabolism distribution and low toxicity: 1