What do docking and QSAR tell us about the design of HIV-1 reverse transcriptase nonnucleoside inhibitors?

Paneth, Agata; Płonka, W; Paneth, Piotr

doi:10.1007/s00894-017-3489-3

Cited by 7 publications

(7 citation statements)

References 30 publications

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“…Machine Learning techniques like Random Forests outperform significantly other methods such as molecular dynamics, docking, and classical QSAR. While our previous studies [ 28 , 29 ] involving similar techniques hinted at Random Forest performing much better than classical QSAR in the modeling of the docking scores we were unable to sufficiently support such a conclusion due to the limited number of compounds studied. Our present results provide clear evidence that Random Forests calculations trained on docking results can provide an improved scientific tool with better rate and precision of predictions that allow evaluation of properties of hundreds of thousands of compounds in a realistic time.…”

Section: Discussionmentioning

confidence: 59%

Machine Learning augmented docking studies of aminothioureas at the SARS-CoV-2—ACE2 interface

et al. 2021

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The current pandemic outbreak clearly indicated the urgent need for tools allowing fast predictions of bioactivity of a large number of compounds, either available or at least synthesizable. In the computational chemistry toolbox, several such tools are available, with the main ones being docking and structure-activity relationship modeling either by classical linear QSAR or Machine Learning techniques. In this contribution, we focus on the comparison of the results obtained using different docking protocols on the example of the search for bioactivity of compounds containing N-N-C(S)-N scaffold at the S-protein of SARS-CoV-2 virus with ACE2 human receptor interface. Based on over 1800 structures in the training set we have predicted binding properties of the complete set of nearly 600000 structures from the same class using the Machine Learning Random Forest Regressor approach.

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Section: Discussionmentioning

confidence: 59%

Machine Learning augmented docking studies of aminothioureas at the SARS-CoV-2—ACE2 interface

et al. 2021

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“…The procedure used in docking followed that previously reported for the FlexX scoring function [32]. In short, 48 ligands were docked to the allosteric cavity of 107 HIV-1 RT enzymes (available from the PDB [33]), and an average binding score for a given ligand was obtained separately for the wild type (wt) and mutated enzyme.…”

Section: Resultsmentioning

confidence: 99%

“…For this purpose, the ADMEWORKS ModelBuilder was used [35,36]. Due to the size of the training set, sets of 6 descriptors were chosen, as in our previous work [32]. The substructures contained in both sets are shown in Table 3.…”

Section: Resultsmentioning

confidence: 99%

Assessment of Nonnucleoside Inhibitors Binding to HIV-1 Reverse Transcriptase Using HYDE Scoring

Paneth

Płonka²,

Paneth

2019

Pharmaceuticals

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In this study, 48 inhibitors were docked to 107 allosteric centers of human immunodeficiency virus 1 (HIV-1) reverse transcriptase from the Protein Data Bank (PDB). Based on the average binding scores, quantitative structure-activity relationship (QSAR) equations were constructed in order to elucidate directions of further development in the design of inhibitors. Such developments, informed by structural data, must have a focus on activity against mutated forms of the enzyme, which are the cause of the emergence of multidrug-resistant viral strains. Docking studies employed the HYDE scoring function. Two types of QSARs have been considered: One based on topological descriptors and the other on structural fragments of the inhibitors. Both methods gave similar results, indicating substructures favoring binding to mutated forms of the enzyme.

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“…We wanted to investigate the relationships between activity and general physicochemical properties of the compounds, general structural features, as well as features specific to our molecules. We employed similar techniques used in the past [41,42], which proved successful. We chose the Random Forest Regressor as a modeling algorithm, due to its ability to handle non-linear relations and the possibility of learning from a small dataset with a large number of features.…”

Section: Discussionmentioning

confidence: 99%

Docking and QSAR of Aminothioureas at the SARS-CoV-2 S-Protein–Human ACE2 Receptor Interface

Płonka

Paneth

2020

Molecules

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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.

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What do docking and QSAR tell us about the design of HIV-1 reverse transcriptase nonnucleoside inhibitors?

Cited by 7 publications

References 30 publications

Machine Learning augmented docking studies of aminothioureas at the SARS-CoV-2—ACE2 interface

Machine Learning augmented docking studies of aminothioureas at the SARS-CoV-2—ACE2 interface

Assessment of Nonnucleoside Inhibitors Binding to HIV-1 Reverse Transcriptase Using HYDE Scoring

Docking and QSAR of Aminothioureas at the SARS-CoV-2 S-Protein–Human ACE2 Receptor Interface

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