QSAR Modeling of Imbalanced High-Throughput Screening Data in PubChem

Zakharov, Alexey; Peach, Megan L.; Sitzmann, Markus; Nicklaus, Marc C.

doi:10.1021/ci400737s

Cited by 104 publications

(112 citation statements)

References 26 publications

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“…Consequently, when trying to predict a minority class in an unbalanced data set, machine learning methods are prone to assign most samples to the majority class, resulting in a large number of erroneous predictions for minority class. 36 …”

Section: Resultsmentioning

confidence: 99%

Discovery of New Anti-Schistosomal Hits by Integration of QSAR-Based Virtual Screening and High Content Screening

et al. 2016

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Schistosomiasis is a debilitating neglected tropical disease, caused by flatworms of Schistosoma genus. The treatment relies on a single drug, praziquantel (PZQ), making the discovery of new compounds extremely urgent. In this work, we integrated QSAR-based virtual screening (VS) of Schistosoma mansoni thioredoxin glutathione reductase (SmTGR) inhibitors and high content screening (HCS) aiming to discover new antischistosomal agents. Initially, binary QSAR models for inhibition of SmTGR were developed and validated using the Organization for Economic Co-operation and Development (OECD) guidance. Using these models, we prioritized 29 compounds for further testing in two HCS platforms based on image analysis of assay plates. Among them, 2-[2-(3-methyl-4-nitro-5-isoxazolyl)vinyl]pyridine and 2-(benzylsulfonyl)-1,3-benzothiazole, two compounds representing new chemical scaffolds have activity against schistosomula and adult worms at low micromolar concentrations and therefore represent promising antischistosomal hits for further hit-to-lead optimization.

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Section: Resultsmentioning

confidence: 99%

Discovery of New Anti-Schistosomal Hits by Integration of QSAR-Based Virtual Screening and High Content Screening

et al. 2016

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“…This imbalanced nature of HTS data presents a great challenge for developing an accurate prediction model from them. 91-94 This issue may be addressed by generating a balanced data set through resampling of the original HTS data set. Several studies 33,91-94 have applied different resampling techniques for analysis of HTS data in PubChem.…”

Section: Dealing With Data Imbalance Issues In Pubchem Datamentioning

confidence: 99%

“…91-94 This issue may be addressed by generating a balanced data set through resampling of the original HTS data set. Several studies 33,91-94 have applied different resampling techniques for analysis of HTS data in PubChem. They are broadly categorized into two classes: undersampling of the majority class (inactive compounds) and oversampling of the minority class (active compounds).…”

Section: Dealing With Data Imbalance Issues In Pubchem Datamentioning

confidence: 99%

Getting the most out of PubChem for virtual screening

Kim

2016

Expert Opinion on Drug Discovery

134

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Introduction With the emergence of the “big data” era, the biomedical research community has great interest in exploiting publicly available chemical information for drug discovery. PubChem is an example of public databases that provide a large amount of chemical information free of charge. Areas covered This article provides an overview of how PubChem’s data, tools, and services can be used for virtual screening and reviews recent publications that discuss important aspects of exploiting PubChem for drug discovery. Expert opinion PubChem offers comprehensive chemical information useful for drug discovery. It also provides multiple programmatic access routes, which are essential to build automated virtual screening pipelines that exploit PubChem data. In addition, PubChemRDF allows users to download PubChem data and load them into a local computing facility, facilitating data integration between PubChem and other resources. PubChem resources have been used in many studies for developing bioactivity and toxicity prediction models, discovering polypharmacologic (multi-target) ligands, and identifying new macromolecule targets of compounds (for drug-repurposing or off-target side effect prediction). These studies demonstrate the usefulness of PubChem as a key resource for computer-aided drug discovery and related area.

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“…Three-dimensional conformers of most chemical molecules are also available [21]. PubChem data have been used in various researches including Quantitative Structure-Activity relationship (QSAR) studies [21], [22].…”

Section: Rungsang Nakrumpaimentioning

confidence: 99%

Study of Contribution of Chemical and Physical Properties of Molecules towards Their Activities against Ebola Virus Using Data Mining Techniques

R¹

2017

IJMLC

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Abstract-Ebola virus disease is a highly fatal hemorrhagic disease. To date, there is no approved drug to cure Ebola virus disease. Thus there is still the need to search for an effective drug. This study used various data mining techniques to investigate the relationship of chemical and physical properties of chemical molecules towards their activities against Ebola virus. The results suggest that these properties contribute to their activities against Ebola virus. Thus they could be used together with bioinformatics and cheminformatics techniques to help speed up the drug discovery process of Ebola virus disease.

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QSAR Modeling of Imbalanced High-Throughput Screening Data in PubChem

Cited by 104 publications

References 26 publications

Discovery of New Anti-Schistosomal Hits by Integration of QSAR-Based Virtual Screening and High Content Screening

Discovery of New Anti-Schistosomal Hits by Integration of QSAR-Based Virtual Screening and High Content Screening

Getting the most out of PubChem for virtual screening

Study of Contribution of Chemical and Physical Properties of Molecules towards Their Activities against Ebola Virus Using Data Mining Techniques

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