AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings

Eberhardt, Jérôme; Santos-Martins, Diogo; Tillack, Andreas F.; Forli, Stefano

doi:10.26434/chemrxiv.14774223.v1

Cited by 117 publications

(148 citation statements)

References 34 publications

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“…The results are summarized in terms of a Receiver Operating Curve (ROC) shown in Figure 4. The naïve method performs decently as characterized by an Area Under the Curve (AUC) of 0.71, which is in good agreement with previously published performance of Vina (AUC = 0.72) (21). Remarkably, the ensemble method showed a significant improvement in performance with an AUC of 0.84, aided by molecular simulations in explicit water.…”

Section: Resultssupporting

confidence: 89%

In silico screening and testing of FDA approved small molecules to block SARS-CoV-2 entry to the host cell by inhibiting Spike protein cleavage

Özdemir

Yıldırım

et al. 2022

Preprint

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The COVID-19 pandemic began in 2019, but it is still active. The development of an effective vaccine reduced the number of deaths; however, a treatment is still needed. Here, we aimed to inhibit viral entry to the host cell by inhibiting Spike (S) protein cleavage by several proteases. We develop a computational pipeline to repurpose FDA-approved drugs to inhibit protease activity and thus prevent S protein cleavage. We tested some of our drug candidates and demonstrated a decrease in protease activity. We believe our pipeline will be beneficial in identifying a drug regimen for COVID-19 patients.

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Section: Resultssupporting

confidence: 89%

In silico screening and testing of FDA approved small molecules to block SARS-CoV-2 entry to the host cell by inhibiting Spike protein cleavage

Özdemir

Yıldırım

et al. 2022

Preprint

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“…[78][79][80] All docking processes were carried out using Autodock vina v.1.2.0. [81,82] The target, AChE was prepared via Discovery Studio (DS) 3.5 [83] having to remove the undesired molecules such as cocrystal ligand (galantamine) and water, and the protonation of missing amino acid residues were made. Afterward, both protein structures (AChE and α-Gly) and the potent compounds (2e, 3d, 3e, and 3f) were energy minimized using CHARMm [84] force field.…”

Section: Molecular Docking Calculationsmentioning

confidence: 99%

Benzimidazolium salts bearing the trifluoromethyl group as organofluorine compounds: Synthesis, characterization, crystal structure, in silico study, and inhibitory profiles against acetylcholinesterase and α‐glycosidase

Tezcan

Gök

Sevinçek

et al. 2022

J Biochem & Molecular Tox

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Here, we report the synthesis, characterization, and biological activities of a series of benzimidazolium salts bearing the trifluoromethylbenzyl group. All benzimidazolium salts were characterized by using nuclear magnetic resonance (NMR) ( 1 H NMR and 13 C NMR), Fourier transform-infrared spectroscopy, and elemental analysis techniques. The crystal structures of some of these compounds were obtained by the single-crystal X-ray diffraction method. Furthermore, the acetylcholinesterase (AChE) and α-glycosidase (α-Gly) enzyme inhibition activities of these compounds were investigated. The obtained results revealed that 2e, with K i value of 1.36 ± 0.34 µM against AChE and 3d with K i value of 91.37 ± 10.38 µM against α-Gly, were the most potent compounds against both assigned enzymes. It should be noted that most of the synthesized compounds were more potent than standard inhibitor tacrine (TAC) against AChE. In silico studies, we focused on compound 2e, 3d, 3e, and 3f as potent inhibitors of AChE and α-Gly, the compound 2e showed good binding energy (−10.23 kcal/mol), among the three selected compounds and positive control (−10.18, −10.08, and −7.37 kcal/mol for 3d, 3f, and TAC, respectively). Likewise, as a result of the same compounds against the α-Gly enzyme, the compound 3d had the highest binding affinity (−8.39 kcal/mol) between the four selected compounds and the positive control (−8.27, −8.10, −8.06, and −7.53 kcal/mol for 3f, 3e, 2e, and acarbose, respectively). From the absorption, distribution, metabolism, excretion, and toxicity analyses, it can be concluded that the compounds under consideration exhibited more drug-likeness properties in the prediction studies compared to positive controls.

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“…Baselines For docking power benchmark, the baselines are DeepDock [61] and the top 10 scoring functions reported in [28], including both conventional scoring functions and machine learningbased ones. For the binding pose accuracy, the baselines are Autodock Vina [63,64], Vinardo [65], Smina [66], and AutoDock4 [67].…”

Section: Protein-ligand Binding Pose Predictionmentioning

confidence: 99%

“…Results From the docking power benchmark results shown in Figure 3, Uni-Mol ranks the 1st, with the top 1 success rate of 91.6%. For comparison, the previous top scoring function AutoDock Vina [63,64] achieves 90.2% of the top 1 success rate in this benchmark. From the binding pose accuracy results shown in Table 5, Uni-Mol also surpasses all other baselines.…”

Section: Protein-ligand Binding Pose Predictionmentioning

confidence: 99%

Uni-Mol: A Universal 3D Molecular Representation Learning Framework

Zhou

Gao

Ding

et al. 2022

Preprint

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Molecular representation learning (MRL) has gained tremendous attention due to its critical role in learning from limited supervised data for applications like drug design. In most MRL methods, molecules are treated as 1D sequential tokens or 2D topology graphs, limiting their ability to incorporate 3D information for downstream tasks and, in particular, making it almost impossible for 3D geometry prediction or generation. Herein, we propose Uni-Mol, a universal MRL framework that significantly enlarges the representation ability and application scope of MRL schemes. Uni-Mol is composed of two models with the same SE(3)-equivariant transformer architecture: a molecular pretraining model trained by 209M molecular conformations; a pocket pretraining model trained by 3M candidate protein pocket data. The two models are used independently for separate tasks, and are combined when used in protein-ligand binding tasks. By properly incorporating 3D information, Uni-Mol outperforms SOTA in 14/15 molecular property prediction tasks. Moreover, Uni-Mol achieves superior performance in 3D spatial tasks, including protein-ligand binding pose prediction, molecular conformation generation, etc. Finally, we show that Uni-Mol can be successfully applied to the tasks with few-shot data like pocket druggability prediction. The model and data will be made publicly available at \url{https://github.com/dptech-corp/Uni-Mol}

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AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings

Cited by 117 publications

References 34 publications

In silico screening and testing of FDA approved small molecules to block SARS-CoV-2 entry to the host cell by inhibiting Spike protein cleavage

In silico screening and testing of FDA approved small molecules to block SARS-CoV-2 entry to the host cell by inhibiting Spike protein cleavage

Benzimidazolium salts bearing the trifluoromethyl group as organofluorine compounds: Synthesis, characterization, crystal structure, in silico study, and inhibitory profiles against acetylcholinesterase and α‐glycosidase

Uni-Mol: A Universal 3D Molecular Representation Learning Framework

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