Similar, or dissimilar, that is the question. How different are methods for comparison of compounds similarity?

Krzysztof, Rajda,; Podlewska, Sabina

doi:10.1016/j.compbiolchem.2020.107367

Cited by 5 publications

(5 citation statements)

References 38 publications

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“…It is well-known that "the results of similarity assessment vary depending on the compound representation and metric" [10][11][12]. Willett carried out a detailed comparison of a large number of similarity coefficients and established that the "well-known Tanimoto coefficient remains the method of choice for the computation of fingerprint-based similarity" [13].…”

Section: Introductionmentioning

confidence: 99%

Extended similarity indices: the benefits of comparing more than two objects simultaneously. Part 2: speed, consistency, diversity selection

et al. 2021

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Despite being a central concept in cheminformatics, molecular similarity has so far been limited to the simultaneous comparison of only two molecules at a time and using one index, generally the Tanimoto coefficent. In a recent contribution we have not only introduced a complete mathematical framework for extended similarity calculations, (i.e. comparisons of more than two molecules at a time) but defined a series of novel idices. Part 1 is a detailed analysis of the effects of various parameters on the similarity values calculated by the extended formulas. Their features were revealed by sum of ranking differences and ANOVA. Here, in addition to characterizing several important aspects of the newly introduced similarity metrics, we will highlight their applicability and utility in real-life scenarios using datasets with popular molecular fingerprints. Remarkably, for large datasets, the use of extended similarity measures provides an unprecedented speed-up over “traditional” pairwise similarity matrix calculations. We also provide illustrative examples of a more direct algorithm based on the extended Tanimoto similarity to select diverse compound sets, resulting in much higher levels of diversity than traditional approaches. We discuss the inner and outer consistency of our indices, which are key in practical applications, showing whether then-ary and binary indices rank the data in the same way. We demonstrate the use of the newn-ary similarity metrics ont-distributed stochastic neighbor embedding (t-SNE) plots of datasets of varying diversity, or corresponding to ligands of different pharmaceutical targets, which show that our indices provide a better measure of set compactness than standard binary measures. We also present a conceptual example of the applicability of our indices in agglomerative hierarchical algorithms. The Python code for calculating the extended similarity metrics is freely available at:https://github.com/ramirandaq/MultipleComparisons

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Section: Introductionmentioning

confidence: 99%

Extended similarity indices: the benefits of comparing more than two objects simultaneously. Part 2: speed, consistency, diversity selection

et al. 2021

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“…To prove the uniqueness of the new compound library (both SMBL-V and SMBL-E), we performed a similarity comparison 30 , 31 between our libraries and several commercially available libraries using a 2D fingerprint Tanimoto coefficient (Tc) calculation (Fig. 1c ).…”

Section: Resultsmentioning

confidence: 99%

Construction of a synthetic methodology-based library and its application in identifying a GIT/PIX protein–protein interaction inhibitor

Peng

Guo

et al. 2022

Nat Commun

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In recent years, the flourishing of synthetic methodology studies has provided concise access to numerous molecules with new chemical space. These compounds form a large library with unique scaffolds, but their application in hit discovery is not systematically evaluated. In this work, we establish a synthetic methodology-based compound library (SMBL), integrated with compounds obtained from our synthetic researches, as well as their virtual derivatives in significantly larger scale. We screen the library and identify small-molecule inhibitors to interrupt the protein–protein interaction (PPI) of GIT1/β-Pix complex, an unrevealed target involved in gastric cancer metastasis. The inhibitor 14-5-18 with a spiro[bicyclo[2.2.1]heptane-2,3’-indolin]−2’-one scaffold, considerably retards gastric cancer metastasis in vitro and in vivo. Since the PPI targets are considered undruggable as they are hard to target, the successful application illustrates the structural specificity of SMBL, demonstrating its potential to be utilized as compound source for more challenging targets.

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“…In the field of cheminformatics to screening similar molecules is a smart practice, as the assumption underlies a fundamental principle that chemical compounds with similar structures should elicit similar biological activities [48]. The 'Find Similar Molecules by Fingerprints' protocol in DS provides a Tanimoto coefficient (Tc), which was adopted as the evaluation criterion to find similarity of ligands in an input library with the reference ligand that is Lanifibranor as it is a single drug that targets PPAR altogether.…”

Section: Similarity Searchmentioning

confidence: 99%

Deciphering the relational dynamics of AF-2 domain of PAN PPAR through drug repurposing and comparative simulations

2023

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Peroxisome proliferator-activated receptors (PPARs) are nuclear receptors, and their activation has been proven to treat mild liver fibrosis, reduce steatosis, inflammation, and the extrahepatic effects of chronic liver disease. Considering the significance of the PPARs, it is targeted for the treatment of Non-Alcoholic Steatohepatitis (NASH), for which currently there is no FDA-approved drug. Lanifibranor is a next-generation highly potential indole sulfonamide derivative that is presently in clinical trial phase III as an anti-NASH drug which fully activates PPARα and PPARδ and partially activates PPARγ. In the current study, a comprehensive computational investigation including 3D-QSAR pharmacophore modeling, MD simulations and binding free energy calculations is performed to get insights into the activation mechanism of the Lanifibranor. Furthermore, FDA-approved drugs were explored for repurposing through virtual screening against each PPAR pharmacophore to identify potential drug candidates. Forasartan, Raltitrexed, and Lifitegrast stood out as potential agonists for PPARα (full agonist), PPARγ (partial agonist), and PPARδ (full agonist), respectively. The findings of the study highlighted a lack of hydrogen bond acceptor feature in Raltitrexed and Lanifibranor which is responsible for partial activation of PPARγ that plays a critical role in preventing lipid accumulation. In addition to this, the significant role of AF2 domain in full and partial activation of PPARs through electrostatic interactions was also revealed, that facilitates the anchoring of ligand within the binding cavity. Moreover, common chemical scaffolds (methyl sulfonyl benzene, butyric acid, and chlorobenzene) identified using Fingerprinting technique were presented in this study which hold the potential to aid in the design and development of target specific novel Pan PPAR medications in future.

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Similar, or dissimilar, that is the question. How different are methods for comparison of compounds similarity?

Cited by 5 publications

References 38 publications

Extended similarity indices: the benefits of comparing more than two objects simultaneously. Part 2: speed, consistency, diversity selection

Extended similarity indices: the benefits of comparing more than two objects simultaneously. Part 2: speed, consistency, diversity selection

Construction of a synthetic methodology-based library and its application in identifying a GIT/PIX protein–protein interaction inhibitor

Deciphering the relational dynamics of AF-2 domain of PAN PPAR through drug repurposing and comparative simulations

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