Local Interaction Density (LID), a Fast and Efficient Tool to Prioritize Docking Poses

Jacquemard, Célien; Tran-Nguyen, Viet-Khoa; Drwal, Malgorzata N.; Rognan, Didier; Kellenberger, Esther

doi:10.3390/molecules24142610

Cited by 5 publications

(4 citation statements)

References 37 publications

(44 reference statements)

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“…The former translates the observed protein-ligand interactions into pharmacophoric features and quantitative structure-activity relationship (QSAR) models [ 39 , 69 , 70 ], which have been used for several applications, such as VS, the profiling of ligands, the analysis of pseudo-receptors, and de novo designs [ 71 , 72 , 73 , 74 ]. On the other hand, the combination of molecular similarity and docking techniques has been examined in the last years as an alternative procedure to assess the reliability of predicted poses of ligands by measuring the overlay against suitable templates [ 75 , 76 , 77 , 78 , 79 , 80 ].…”

Section: Lb and Sb Strategies In Vsmentioning

confidence: 99%

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Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches

Vázquez

Lopez

Gibert³

et al. 2020

Molecules

121

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Virtual screening (VS) is an outstanding cornerstone in the drug discovery pipeline. A variety of computational approaches, which are generally classified as ligand-based (LB) and structure-based (SB) techniques, exploit key structural and physicochemical properties of ligands and targets to enable the screening of virtual libraries in the search of active compounds. Though LB and SB methods have found widespread application in the discovery of novel drug-like candidates, their complementary natures have stimulated continued efforts toward the development of hybrid strategies that combine LB and SB techniques, integrating them in a holistic computational framework that exploits the available information of both ligand and target to enhance the success of drug discovery projects. In this review, we analyze the main strategies and concepts that have emerged in the last years for defining hybrid LB + SB computational schemes in VS studies. Particularly, attention is focused on the combination of molecular similarity and docking, illustrating them with selected applications taken from the literature.

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Section: Lb and Sb Strategies In Vsmentioning

confidence: 99%

“…( c ) The ROCS score is based on the Tversky coefficient. Reprinted with permission from Springer Nature [ 79 ].…”

Section: Figurementioning

confidence: 99%

Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches

Vázquez

Lopez

Gibert³

et al. 2020

Molecules

121

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show abstract

“…Our proposed protocol is to first screen with ligand-based models, because they are so fast. However, at the end of a structure-based virtual screening pipeline, ligands should be available as protein–ligand complexes, so that other types of structure-based filtering is also possible (rescoring, − pharmacophore filter, molecular dynamics simulation after docking, etc.). Also, experiments show that docking usually has a better very early enrichment (EF 1% ) than the corresponding regressor (Table ).…”

Section: Resultsmentioning

confidence: 99%

Lean-Docking: Exploiting Ligands’ Predicted Docking Scores to Accelerate Molecular Docking

Berenger

Kumar

Zhang

et al. 2021

J. Chem. Inf. Model.

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In structure-based virtual screening (SBVS), a binding site on a protein structure is used to search for ligands with favorable nonbonded interactions. Because it is computationally difficult, docking is time-consuming and any docking user will eventually encounter a chemical library that is too big to dock. This problem might arise because there is not enough computing power or because preparing and storing so many three-dimensional (3D) ligands requires too much space. In this study, however, we show that quality regressors can be trained to predict docking scores from molecular fingerprints. Although typical docking has a screening rate of less than one ligand per second on one CPU core, our regressors can predict about 5800 docking scores per second. This approach allows us to focus docking on the portion of a database that is predicted to have docking scores below a user-chosen threshold. Herein, usage examples are shown, where only 25% of a ligand database is docked, without any significant virtual screening performance loss. We call this method “lean-docking”. To validate lean-docking, a massive docking campaign using several state-of-the-art docking software packages was undertaken on an unbiased data set, with only wet-lab tested active and inactive molecules. Although regressors allow the screening of a larger chemical space, even at a constant docking power, it is also clear that significant progress in the virtual screening power of docking scores is desirable.

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“…Although these ligands represent a vast amount of information, they are generally not used explicitly in most docking algorithms, which rely primarily on protein structures. Some docking algorithms , are able to consider the similarity to a single reference ligand to improve the prediction of the pose of the docked ligand, and there are methods , that allow rescoring of the docked poses of fragments and ligands based on their similarity to a single or multiple known reference ligands.…”

Section: Introductionmentioning

confidence: 99%

ProBiS-Dock: A Hybrid Multitemplate Homology Flexible Docking Algorithm Enabled by Protein Binding Site Comparison

Konc

Lešnik

Škrlj

et al. 2022

J. Chem. Inf. Model.

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The protein data bank (PDB) is a rich source of protein ligand structures, but ligands are not explicitly used in current docking algorithms. We have developed ProBiS-Dock, a docking algorithm complementary to the ProBiS-Dock Database (J. Chem. Inf. Model. 2021, 61, 4097−4107) that treats small molecules and proteins as fully flexible entities and allows conformational changes in both after ligand binding. A new scoring function is described that consists of a binding site-specific scoring function (ProBiS-Score) and a general statistical scoring function. ProBiS-Dock enables rapid docking of small molecules to proteins and has been successfully validated in silico against standard benchmarks. It enables rapid search for new active ligands by leveraging existing knowledge in the PDB. The potential of the software for drug development has been confirmed in vitro by the discovery of new inhibitors of human indoleamine 2,3dioxygenase 1, an enzyme that is an attractive target for cancer therapy and catalyzes the first rate-determining step of L-tryptophan metabolism via the kynurenine pathway. The software is freely available to academic users at http://insilab.org/probisdock.

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Local Interaction Density (LID), a Fast and Efficient Tool to Prioritize Docking Poses

Cited by 5 publications

References 37 publications

Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches

Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches

Lean-Docking: Exploiting Ligands’ Predicted Docking Scores to Accelerate Molecular Docking

ProBiS-Dock: A Hybrid Multitemplate Homology Flexible Docking Algorithm Enabled by Protein Binding Site Comparison

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