Similarity maps - a visualization strategy for molecular fingerprints and machine-learning methods

Riniker, Sereina; Landrum, Gregory A.

doi:10.1186/1758-2946-5-43

Cited by 174 publications

(178 citation statements)

References 21 publications

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“…Atoms and fragments promoting the inhibition are highlighted by green (Figure 1), atoms and fragments decreasing the inhibitory potential are highlighted by purple (Figure 2), and gray lines (Figures 1 and 2) delimit the region of split between the favorable and the unfavorable contributions. 50 …”

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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“…[8] The calculated descriptors include variants of Morgan fingerprints, Molecular ACCess System (MACCS) keys [9] and other molecular descriptors. The Morgan fingerprints are circular fingerprints, implemented according to the Morgan algorithm [10,11] on extended connectivity fingerprinting. The circular fingerprints consider the neighbourhood of each atom up to a certain bond radius.…”

Section: Descriptorsmentioning

confidence: 99%

HToPred: A Tool for Human Topoisomerase II Inhibitor Prediction

Tripathi

Shaikh

Bharatam

et al. 2018

Molecular Informatics

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The enzyme human topoisomerase IIα (hTopoIIα) is an important anticancer drug target. Due to the availability of multiple inhibitor-binding sites in this enzyme, the anti-hTopoII agents possess high chemical diversity. Chemoinformatics methods can be used to identify lead compounds from large databases for hTopoII inhibitory activity and classify them. In this work, we report the use of machine learning methods to develop classification models for the identification of possible anti-hTopoIIα agents and to classify them as catalytic inhibitors vs. poisons. Initially, an extensive dataset of small molecules which are reported to be evaluated towards hTopoIIα inhibition was collected from ChEMBL database and literature. Using this dataset, predictive models for classifying small molecules into hTopoIIα inhibitors and non-inhibitors were developed. Additionally, the model development was taken up for the prediction of the type of hTopoIIα inactivation. Several molecular fingerprints and physicochemical descriptors of the molecules in the dataset were calculated using the chemoinformatics tool RDKit. Various classifiers were evaluated to establish suitable protocol. Further, ensemble models were developed by bagging of homogenous classifier and selective fusion of heterogeneous classifiers. The models were thoroughly validated with 5-fold cross validation and external validation. The best performing models were incorporated into a tool christened as Human Topoisomerase IIα Inhibitor Prediction (HToPred, http://14.139.57.41/HToPred). A molecular docking based validation for the successful application of HToPred in predicting the mode of enzyme inhibition was performed, which further established the acceptability of this tool. This tool can serve as an important platform to prescreen compounds for anti-hTopoIIα potential.

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“…52 (1) front-end using the responsive web-based templates in HTML5 and JavaScript for the user to draw/paste the chemical; (2) RDKit ECFP-4 fingerprint (Morgan) representation is calculated on the back-end; (3) scikit-learn API machine learning model runs on the back-end; (4) the front-end responsive framework templates based on Flask is rendered with the models predictions and the probability maps. 51 computational methods for addressing toxicity potential of chemicals is of upmost importance. Machine learning methods, such as modern QSAR approaches have become more powerful due to the rapid expansion of bioactivity and toxicity data available in chemical databases such as TOXNET, 67,68 ChEMBL, 69 and PubChem.…”

Section: Final Remarksmentioning

confidence: 99%

“…51,52 All the functionalities are orchestrated by Flask (back-front-end), which is responsible to call the individual modules in the back-end and interact with the user in the front-end, using the responsive web-based templates in HTML5 and JavaScript (front-end). The features described here were used to develop the two innovative web apps, named Pred-hERG, to identify possible hERG blockers and non-blockers, and Pred-Skin, for assessment of skin sensitization potential of chemicals (see Pred-hERG and Pred-Skin sections).…”

Section: Development Of Web-based and Mobile Applicationsmentioning

confidence: 99%

Development of Web and Mobile Applications for Chemical Toxicity Prediction

Alves¹,

Braga²,

Muratov³

et al. 2018

J. Braz. Chem. Soc.

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Computational tools are recognized to provide high-quality predictions for the assessment of chemical toxicity. In the recent years, mobile devices have become ubiquitous, allowing for the development of innovative and useful models implemented as chemical software applications. Here, we will briefly discuss this recent uptick in the development of web-based and mobile applications for chemical problems, focusing on best practices, development, usage and interpretation. As an example, we also describe two innovative apps (Pred-hERG and Pred-Skin) for chemical toxicity prediction developed in our laboratory. These applications are based on predictive quantitative structure-activity relationships (QSAR) models developed using the largest publicly available datasets of structurally diverse compounds. The developed tools ensure both highly accurate predictions and easy interpretation of the models, allowing users to discriminate potential toxicants and to purpose structural modifications to design safer chemicals.

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Similarity maps - a visualization strategy for molecular fingerprints and machine-learning methods

Cited by 174 publications

References 21 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

HToPred: A Tool for Human Topoisomerase II Inhibitor Prediction

Development of Web and Mobile Applications for Chemical Toxicity Prediction

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