VSPrep: A General KNIME Workflow for the Preparation of Molecules for Virtual Screening

Gally, José-Manuel; Bourg, Stéphane; Do, Quoc‐Tuan; Aci-Sèche, Samia; Bonnet, Pascal

doi:10.1002/minf.201700023

Cited by 28 publications

(22 citation statements)

References 79 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…NP also display a great diversity in their scaffolds (Lee and Schneider, 2001;Grabowski et al, 2008;Yongye et al, 2012). For instance, the GreenPharma database (Do et al, 2015;Gally et al, 2017) contains 55,185 Murcko frameworks (Bemis and Murcko, 1996) for 302,000 natural products (18.3%), whereas NCI and ChEMBL databases contain a little of less NP-derived scaffolds: 13.1 and 13.6% respectively.…”

Section: Limitations Related To the Need Of Market-compatible Amountsmentioning

confidence: 99%

“…de/supernatural) with more than 325,500 natural compounds, offering 3D structure and conformers (Banerjee et al, 2015) which seems to outperform many others (Harvey et al, 2015;Xie et al, 2015). Another source of natural compounds is also the Greenpharma collection (www.greenpharma.com/products/ compound-librairies/#GPNDB) (Do et al, 2015;Gally et al, 2017).…”

Section: At the Development Stage: Systematic Inventories Of Natural mentioning

confidence: 99%

See 1 more Smart Citation

Unraveling Plant Natural Chemical Diversity for Drug Discovery Purposes

Lautie

Russo

Ducrot³

2020

Front. Pharmacol.

156

120

View full text Add to dashboard Cite

The screening and testing of extracts against a variety of pharmacological targets in order to benefit from the immense natural chemical diversity is a concern in many laboratories worldwide. And several successes have been recorded in finding new actives in natural products, some of which have become new drugs or new sources of inspiration for drugs. But in view of the vast amount of research on the subject, it is surprising that not more drug candidates were found. In our view, it is fundamental to reflect upon the approaches of such drug discovery programs and the technical processes that are used, along with their inherent difficulties and biases. Based on an extensive survey of recent publications, we discuss the origin and the variety of natural chemical diversity as well as the strategies to having the potential to embrace this diversity. It seemed to us that some of the difficulties of the area could be related with the technical approaches that are used, so the present review begins with synthetizing some of the more used discovery strategies, exemplifying some key points, in order to address some of their limitations. It appears that one of the challenges of natural product-based drug discovery programs should be an easier access to renewable sources of plant-derived products. Maximizing the use of the data together with the exploration of chemical diversity while working on reasonable supply of natural product-based entities could be a way to answer this challenge. We suggested alternative ways to access and explore part of this chemical diversity with in vitro cultures. We also reinforced how important it was organizing and making available this worldwide knowledge in an "inventory" of natural products and their sources. And finally, we focused on strategies based on synthetic biology and syntheses that allow reaching industrial scale supply. Approaches based on the opportunities lying in untapped natural plant chemical diversity are also considered.

show abstract

Section: Limitations Related To the Need Of Market-compatible Amountsmentioning

confidence: 99%

Section: At the Development Stage: Systematic Inventories Of Natural mentioning

confidence: 99%

Unraveling Plant Natural Chemical Diversity for Drug Discovery Purposes

Lautie

Russo

Ducrot³

2020

Front. Pharmacol.

156

120

View full text Add to dashboard Cite

show abstract

“…Molecules were standardised using the tool VSPrpep [22], giving rise to a graph representation. Starting from that representation, each chemical compound was described using the RDKit Morgan fingerprint implemented in Knime [9] with a radius of three chemical bonds [3] as used in a previous study [12].…”

Section: Data Preparationmentioning

confidence: 99%

PrePeP

Koptelov

Zimmermann

Bonnet

et al. 2018

Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

Self Cite

View full text Add to dashboard Cite

Pan Assays Interference Compounds (PAINS) are a significant problem in modern drug discovery: compounds showing non-target specific activity in high-throughput screening can mislead medicinal chemists during hit identification, wasting time and resources. Recent work has shown that existing structural alerts are not up to the task of identifying PAINS. To address this shortcoming , we are in the process of developing a tool, PrePeP, that predicts PAINS, and allows experts to visually explore the reasons for the prediction. In the paper, we discuss the different aspects that are involved in developing a functional tool: systematically deriving structural descriptors, addressing the extreme imbalance of the data, offering visual information that pharmacological chemists are familiar with. We evaluate the quality of the approach using benchmark data sets from the literature and show that we correct several shortcomings of existing PAINS alerts that have recently been pointed out.

show abstract

“…Regarding data curation pipelines, a recent publication by Gally et al proposed a workow for preliminary molecule preparation in KNIME. 57 Also, a useful and comprehensive tutorial for KNIME application into chemical data curation has been recently published elsewhere. 58…”

Section: Data Curationmentioning

confidence: 99%