A Practical Guide to Molecular Docking and Homology Modelling for Medicinal Chemists

Lohning, Anna; Levonis, Stephan M; Williams-Noonan, Billy J; Schweiker, Stephanie S

doi:10.2174/1568026617666170130110827

Cited by 125 publications

(75 citation statements)

References 47 publications

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“…Protein-ligand docking is a computational technique that predicts the conformation and orientation (pose) of a ligand when it is bound to a given protein [1,[7][8][9][10][11][12]. With this method, the ligand-target interactions are modeled to achieve an optimal complementarity of steric and physicochemical properties [13].…”

Section: Introductionmentioning

confidence: 99%

Comparing AutoDock and Vina in Ligand/Decoy Discrimination for Virtual Screening

Vieira

Sousa

2019

Applied Sciences

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AutoDock and Vina are two of the most widely used protein-ligand docking programs. The fact that these programs are free and available under an open source license, also makes them a very popular first choice for many users and a common starting point for many virtual screening campaigns, particularly in academia. Here, we evaluated the performance of AutoDock and Vina against an unbiased dataset containing 102 protein targets, 22,432 active compounds and 1,380,513 decoy molecules. In general, the results showed that the overall performance of Vina and AutoDock was comparable in discriminating between actives and decoys. However, the results varied significantly with the type of target. AutoDock was better in discriminating ligands and decoys in more hydrophobic, poorly polar and poorly charged pockets, while Vina tended to give better results for polar and charged binding pockets. For the type of ligand, the tendency was the same for both Vina and AutoDock. Bigger and more flexible ligands still presented a bigger challenge for these docking programs. A set of guidelines was formulated, based on the strengths and weaknesses of both docking program and their limits of validation.of the poses previously created, discriminating between the best and not so good alternatives [1,5]. This estimate, which in some cases is a prediction of the free energy of binding, must be able to discriminate between molecules that bind to the target and those that do not [23]. When looking at the two enantiomers, for example, it is still not possible to identify the most active form with most of the scoring functions used by the most common docking software [24].Even with all the significant improvements in computational power and docking software, considering all interactions that happen when a ligand binds to its target is an extremely challenging task. In order to be rigorous, the scoring functions would have to be much more complex, involve quantum calculations and, thus, these assays would turn out to be considerably expensive and time-consuming. When applying a virtual screening protocol, one wishes to screen very large databases of compounds in a relatively small period of time and, therefore, scoring functions are often simplified to improve the speed and cost of the computational screenings [6]. These simplifications come with a cost in accuracy, which might not be problematic for one ligand-target situation but takes a much more challenging scope when talking about virtual screening of thousands or millions of compounds [5].The goal of virtual screening (VS) is to guide the selection of molecules for experimental testing. In these assays, millions of compounds are docked into one specific target and only a selection of the top scores proceeds for experimental testing. If a scoring functions fails to identify a potential strong binder, then, it remains hidden among those million compounds, despite their pharmacological potential. In fact, that is one of the main problems in VS, the false negatives, or molecules that the docki...

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Section: Introductionmentioning

confidence: 99%

Comparing AutoDock and Vina in Ligand/Decoy Discrimination for Virtual Screening

Vieira

Sousa

2019

Applied Sciences

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“…To further elucidate the binding mode of the Thio‐DPPY derivatives with the EGFR T790M enzyme, a docking study was performed using Autodock 4.2 software . The representative structures of 14a , 14b , 14c , and 14e were docked into the crystal structure of the EGFR T790M binding pocket (PDB: 3IKA), respectively .…”

Section: Resultsmentioning

confidence: 99%

Structural optimization of diphenylpyrimidine scaffold as potent and selective epidermal growth factor receptor inhibitors against L858R/T790M resistance mutation in nonsmall cell lung cancer

Wang

Zhao

et al. 2018

Chem Biol Drug Des

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A new class of thiodiphenylpyrimidine analogs (Thio-DPPY) were synthesized as potent and selective EGFR T790M inhibitors to overcome gefitinib resistance in nonsmall cell lung cancer (NSCLC). This structural optimization led to the identification of two potent EGFR inhibitors, 14a and 14e, which possess IC values of 27.5 and 9.1 nM, respectively. Moreover, compounds 14a (SI > 36.4) and 14e (SI > 109.9) exhibited high selectivity and low activity against the wild-type EGFR (IC > 1,000 nM). In particular, compound 14a also displayed strong potency against EGFR -mutated H1975 cells (IC = 0.074 μM), but weak activity toward normal cells HBE (IC > 40 μM) and LO-2 (IC = 9.891 μM). It is important that compound 14a (SI = 52.6) significantly improved the selectivity against mutant H1975 cells over wild-type A431 cells than rociletinib (SI = 6.0), thus revealing its slight cell cytotoxicity. This study provides a promising Thio-DPPY derivative as enhanced EGFR T790M inhibitor, and also revealed valuable clues for further optimization of DPPY scaffold to overcome NSCLC resistance.

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“…The poses that showed best ligand-enzyme interactions were selected and stored for energy calculations. 42…”

Section: Metabolomic Analysis Proceduresmentioning

confidence: 99%

Inhibition of SARS-CoV-2 main protease by phenolic compounds from Manilkara hexandra (Roxb.) Dubard assisted by metabolite profiling and in silico virtual screening

et al. 2020

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A Practical Guide to Molecular Docking and Homology Modelling for Medicinal Chemists

Cited by 125 publications

References 47 publications

Comparing AutoDock and Vina in Ligand/Decoy Discrimination for Virtual Screening

Comparing AutoDock and Vina in Ligand/Decoy Discrimination for Virtual Screening

Structural optimization of diphenylpyrimidine scaffold as potent and selective epidermal growth factor receptor inhibitors against L858R/T790M resistance mutation in nonsmall cell lung cancer

Inhibition of SARS-CoV-2 main protease by phenolic compounds from Manilkara hexandra (Roxb.) Dubard assisted by metabolite profiling and in silico virtual screening

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