Benchmarking of HPCC: A novel 3D molecular representation combining shape and pharmacophoric descriptors for efficient molecular similarity assessments

Karaboga, Arnaud Sinan; Petronin, Florent; Marchetti, Gino; Souchet, Michel; Maigret, Bernard

doi:10.1016/j.jmgm.2013.01.003

Cited by 15 publications

(11 citation statements)

References 68 publications

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“…Receiver operating characteristic (ROC) curves are a commonly used method to assess virtual screening performance . A ROC curve is a plot of the true positive rate (TPR, sensitivity) versus the false positive rate (FPR, 1 – specificity).…”

Section: Methodsmentioning

confidence: 99%

LiSiCA: A Software for Ligand-Based Virtual Screening and Its Application for the Discovery of Butyrylcholinesterase Inhibitors

Lešnik

Štular

Brus

et al. 2015

J. Chem. Inf. Model.

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We developed LiSiCA (ligand similarity using clique algorithm)--ligand-based virtual screening software that uses a fast maximum clique algorithm to find two- and three-dimensional similarities between pairs of molecules and applied it to the discovery of novel potent butyrylcholinesterase inhibitors. LiSiCA, which runs in parallel on multiple processor cores, was successfully tested on the Database of Useful Decoys-Enhanced, to evaluate its ability to discriminate active molecules from decoys. We then applied LiSiCA for the discovery of novel inhibitors of human butyrylcholinesterase, a promising anti-Alzheimer target, using a known inhibitor as the reference compound. We demonstrated that LiSiCA is capable of finding potent nanomolar inhibitors, whose scaffolds differed from the reference compound, thus proving its ability for scaffold hopping and usefulness in drug discovery.

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Section: Methodsmentioning

confidence: 99%

LiSiCA: A Software for Ligand-Based Virtual Screening and Its Application for the Discovery of Butyrylcholinesterase Inhibitors

Lešnik

Štular

Brus

et al. 2015

J. Chem. Inf. Model.

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“…These methods are subdivided into two categories: (1) alignment-free methods that are usually computationally faster because they do not require overlapping the molecules or evaluating properties related to the surface (Seddon et al, 2019 ) and (2) alignment-based methods that are computationally costly since these methods superimpose molecular shapes and analyze surface properties, such as polarity and hydrophobicity (Fontaine et al, 2007 ; Kumar and Zhang, 2018 ). Different methods have been used in the representation of the 3D molecular shape of the ligands, such as Gaussian overlay-based methods (Cai et al, 2013 ), atomic distance-based methods (Ballester et al, 2009 ; Ballester, 2011 ; Bonanno and Ebejer, 2020 ), and surface-based methods (Karaboga et al, 2013 ; Cleves et al, 2019 ). The recognized molecular shapes are transformed into the 3D molecular fingerprints that are then compared using similarities or distance indexes, such as Tanimoto, Dice, and Tversky coefficients (Shin et al, 2015 ).…”

Section: Computational Methods Applied In Virtual Screening Approachesmentioning

confidence: 99%

Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products

et al. 2021

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Natural products are continually explored in the development of new bioactive compounds with industrial applications, attracting the attention of scientific research efforts due to their pharmacophore-like structures, pharmacokinetic properties, and unique chemical space. The systematic search for natural sources to obtain valuable molecules to develop products with commercial value and industrial purposes remains the most challenging task in bioprospecting. Virtual screening strategies have innovated the discovery of novel bioactive molecules assessing in silico large compound libraries, favoring the analysis of their chemical space, pharmacodynamics, and their pharmacokinetic properties, thus leading to the reduction of financial efforts, infrastructure, and time involved in the process of discovering new chemical entities. Herein, we discuss the computational approaches and methods developed to explore the chemo-structural diversity of natural products, focusing on the main paradigms involved in the discovery and screening of bioactive compounds from natural sources, placing particular emphasis on artificial intelligence, cheminformatics methods, and big data analyses.

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“…To facilitate measurement of similarity between sets of compounds, many shape similarity methods were complemented with physicochemical properties. Harmonic pharma chemistry coefficient (HPCC) method combined SH shape representation with pharmacophoric features (Karaboga et al, 2013 ). In HPCC method, SH surfaces are discretized as triangle meshes which are assigned pharmacophoric features.…”

Section: D Shape Similarity Methodsmentioning

confidence: 99%

Advances in the Development of Shape Similarity Methods and Their Application in Drug Discovery

2018

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Molecular similarity is a key concept in drug discovery. It is based on the assumption that structurally similar molecules frequently have similar properties. Assessment of similarity between small molecules has been highly effective in the discovery and development of various drugs. Especially, two-dimensional (2D) similarity approaches have been quite popular due to their simplicity, accuracy and efficiency. Recently, the focus has been shifted toward the development of methods involving the representation and comparison of three-dimensional (3D) conformation of small molecules. Among the 3D similarity methods, evaluation of shape similarity is now gaining attention for its application not only in virtual screening but also in molecular target prediction, drug repurposing and scaffold hopping. A wide range of methods have been developed to describe molecular shape and to determine the shape similarity between small molecules. The most widely used methods include atom distance-based methods, surface-based approaches such as spherical harmonics and 3D Zernike descriptors, atom-centered Gaussian overlay based representations. Several of these methods demonstrated excellent virtual screening performance not only retrospectively but also prospectively. In addition to methods assessing the similarity between small molecules, shape similarity approaches have been developed to compare shapes of protein structures and binding pockets. Additionally, shape comparisons between atomic models and 3D density maps allowed the fitting of atomic models into cryo-electron microscopy maps. This review aims to summarize the methodological advances in shape similarity assessment highlighting advantages, disadvantages and their application in drug discovery.

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Benchmarking of HPCC: A novel 3D molecular representation combining shape and pharmacophoric descriptors for efficient molecular similarity assessments

Cited by 15 publications

References 68 publications

LiSiCA: A Software for Ligand-Based Virtual Screening and Its Application for the Discovery of Butyrylcholinesterase Inhibitors

LiSiCA: A Software for Ligand-Based Virtual Screening and Its Application for the Discovery of Butyrylcholinesterase Inhibitors

Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products

Advances in the Development of Shape Similarity Methods and Their Application in Drug Discovery

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