A natural piper-amide-like compound NED-135 exhibits a potent inhibitory effect on the invasive breast cancer cells

Kim, Eun-Sook; Cho, Hyun-Kyung; Lim, Chaemin; Lee, Joo‐Youn; Lee, Dain; Kim, Sanghee; Moon, Aree

doi:10.1016/j.cbi.2015.05.006

Cited by 6 publications

(11 citation statements)

References 37 publications

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“…The ZAP Toolkit has been widely used in the literature to calculate the electrostatic similarity score for two compounds (Boström et al, 2013;Tresadern et al, 2009;Chu and Gochin, 2013;Kim et al, 2015;Kossmann et al, 2016;Woodring et al, 2017;Maccari et al, 2011;Kim et al, 2016;López-Ramos and Perruccio, 2010;Hevener et al, 2012;Fig. 6.…”

Section: Zap Toolkit Accuracy Problemmentioning

confidence: 99%

“…VS applied to the electrostatic similarity of compounds is a clear example of this. Contrary to what happens when VS is applied to select the most similar compounds in shape or pharmacophore properties, where the tools base their predictions on scoring functions that measure these particular features (Lešnik et al, 2015;Puertas-Martín et al, 2019;Yan et al, 2013), the predictions in this field are not exclusively based on this descriptor, but on both the similarity of the three dimensional shape and electrostatic similarity (Tresadern et al, 2009;Chu and Gochin, 2013;Kim et al, 2015;Kossmann et al, 2016;Woodring et al, 2017;Maccari et al, 2011;Kim et al, 2016;López-Ramos and Perruccio, 2010;Hevener et al, 2012;Kaoud et al, 2012;Tiikkainen et al, 2009;Massarotti et al, 2014;Oyarzabal et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

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Optimizing Electrostatic Similarity for Virtual Screening: A New Methodology

Puertas-Martín¹,

Redondo²,

Pérez‐Sánchez³

et al. 2020

Informatica

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Ligand Based Virtual Screening methods are widely used in drug discovery as filters for subsequent in-vitro and in-vivo characterization. Since the databases processed are enormously large, this pre-selection process requires the use of fast and precise methodologies. In this work, the similarity between compounds is measured in terms of electrostatic potential. To do so, we propose a new and alternative methodology, called LBVS-Electrostatic. Accordingly to the obtained results, we are able to conclude that many of the compounds proposed with our novel approach could not be discovered with the classical one.

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Section: Zap Toolkit Accuracy Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimizing Electrostatic Similarity for Virtual Screening: A New Methodology

Puertas-Martín¹,

Redondo²,

Pérez‐Sánchez³

et al. 2020

Informatica

View full text Add to dashboard Cite

show abstract

“…The ZAP Toolkit has been widely used in the literature to calculate the electrostatic similarity score for two compounds. 2,[15][16][17][18][19][20][21][22][23][24][25][26][27]50 In this subsection we would like to remark that the ZAP Toolkit can return an erroneous value, which was discovered when using Optipharm. During the optimization procedure, Optipharm can progressively separate two input compounds aimed to escape from local optima and explore the searching space in depth.…”

Section: Zap Toolkit Accuracy Problemmentioning

confidence: 99%

“…Contrary to what happens when VS is applied to select the most similar compounds in shape or pharmacophore properties, where the tools base their predictions on scoring functions that measure these particular features, 7,13,14 the predictions in this field are not exclusively based on this descriptor, but on both the similarity of the three dimensional shape and electrostatic similarity. [15][16][17][18][19][20][21][22][23][24][25][26][27] Broadly speaking, all the previous works follow the same methodology, although they may differ in the selection procedure used to determine the compounds proposed as best predictions. Essentially, they initially optimize the compounds in the database against the query in terms of shape by using ROCS, 28 they prioritize the top N compounds with the highest shape similarity values and then evaluate them in terms of electrostatic similarity.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Electrostatic Similarity for Virtual Screening: A New Methodology

Martín

Redondo²,

Pérez‐Sánchez³

et al. 2019

Preprint

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<div>Ligand Based Virtual Screening (LBVS) methods are widely used in drug discovery as filters for subsequent in-vitro and in-vivo characterization. This means, increasing accuracy of LBVS approaches may have a huge impact on increasing chances of success. Since the databases processed in drug discovery campaigns are enormously large, this pre-selection process requires the use of fast and precise methodologies. The similarity between compounds can be measured using different descriptors such as shape, pharmacophore or electrostatic similarity. The latter is the goal of this work, i.e., we want to improve the process of obtaining the compounds most similar to a query in terms of electrostatic similarity. To do so, the current and widely proposed methodology in the literature is based on the use of ROCS to assess the similarity of compounds in terms of shape and then evaluate a small subset of them with ZAP for prioritization regarding electrostatic similarity. This paper proposes an alternative methodology that consists of directly optimizing electrostatic similarity and works with the entire database of compounds without using shape cut-offs. For this purpose, a new and improved version of the OptiPharm software has been developed. OptiPharm implements a parameterizable metaheuristic algorithm able to solve any optimization problems directly related to the involved molecular conformations. We show that our new method completely outperforms the classical proposal widely used in the literature. Accordingly, we are able to conclude that many of the compounds proposed with our novel approach could not be discovered with the classical one. As a result, this methodology opens up new horizons in Drug Discovery.</div>

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“…They then carry out a post-processing phase that usually consists of sorting the compounds according to their scoring value, selecting the top of them, and analysing the subset of molecules by extracting or computing values related to the second descriptor of interest. This post-processing phase may be challenging and require much training and knowledge on the part of the expert (Tresadern et al, 2009;Maccari et al, 2011;Chu and Gochin, 2013;Kim et al, 2015;Kossmann et al, 2016;Woodring et al, 2017), basically because most of the time, there are descriptors in conflict, and an improvement in one leads to deterioration in the other.…”

Section: Introductionmentioning

confidence: 99%

MultiPharm-DT: A Multi-Objective Decision Tool for Ligand-Based Virtual Screening Problems

Puertas-Martín¹,

Redondo²,

Ferrández³

et al. 2021

Informatica

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Ligand Based Virtual Screening methods are used to screen molecule databases to select the most promising compounds for a query. This is performed by decision-makers based on the information of the descriptors, which are usually processed individually. This methodology leads to a lack of information and hard post-processing dependent on the expert's knowledge that can end up in the discarding of promising compounds. Consequently, in this work, we propose a new multi-objective methodology called MultiPharm-DT where several descriptors are considered simultaneously and whose results are offered to the decision-maker without effort on their part and without relying on their expertise.

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A natural piper-amide-like compound NED-135 exhibits a potent inhibitory effect on the invasive breast cancer cells

Cited by 6 publications

References 37 publications

Optimizing Electrostatic Similarity for Virtual Screening: A New Methodology

Optimizing Electrostatic Similarity for Virtual Screening: A New Methodology

Optimizing Electrostatic Similarity for Virtual Screening: A New Methodology

MultiPharm-DT: A Multi-Objective Decision Tool for Ligand-Based Virtual Screening Problems

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