Support Vector Machine (SVM) Models for Predicting Inhibitors of the 3′ Processing Step of HIV‐1 Integrase

BackgroundEven though circular fingerprints have been first introduced more than 50 years ago, they are still widely used for building highly predictive, state-of-the-art (Q)SAR models. Historically, these structural fragments were designed to search large molecular databases. Hence, to derive a compact representation, circular fingerprint fragments are often folded to comparatively short bit-strings. However, folding fingerprints introduces bit collisions, and therefore adds noise to the encoded structural information and removes its interpretability. Both representations, folded as well as unprocessed fingerprints, are often used for (Q)SAR modeling.ResultsWe show that it can be preferable to build (Q)SAR models with circular fingerprint fragments that have been filtered by supervised feature selection, instead of applying folded or all fragments. Compared to folded fingerprints, filtered fingerprints significantly increase predictive performance and remain unambiguous and interpretable. Compared to unprocessed fingerprints, filtered fingerprints reduce the computational effort and are a more compact and less redundant feature representation. Depending on the selected learning algorithm filtering yields about equally predictive (Q)SAR models. We demonstrate the suitability of filtered fingerprints for (Q)SAR modeling by presenting our freely available web service Collision-free Filtered Circular Fingerprints that provides rationales for predictions by highlighting important structural features in the query compound (see http://coffer.informatik.uni-mainz.de).ConclusionsCircular fingerprints are potent structural features that yield highly predictive models and encode interpretable structural information. However, to not lose interpretability, circular fingerprints should not be folded when building prediction models. Our experiments show that filtering is a suitable option to reduce the high computational effort when working with all fingerprint fragments. Additionally, our experiments suggest that the area under precision recall curve is a more sensible statistic for validating (Q)SAR models for virtual screening than the area under ROC or other measures for early recognition. Graphical Abstract Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-016-0173-z) contains supplementary material, which is available to authorized users.

show abstract

Quantitative Structure-Activity Relationship Study for HIV-1 LEDGF/p75 Inhibitors

Tian

Yang

et al. 2020

CAD

View full text Add to dashboard Cite

: HIV-1 integrase (IN) is an improtant target for the development of the new anti-AIDS drugs. HIV-1 LEDGF/p75 inhibitors, which blocked the integrase and LEDGF/p75 interaction, have been validated for reduction in HIV-1 viral replicative capacity. In this work, computational quantitative structure–activity relationship (QSAR) models were developed for predicting the bioactivity of HIV-1 integrase LEDGF/p75 inhibitors. We collected 190 inhibitors and their bioactivities in this study, and divided the inhibitors into nine scaffolds by the method of t-distributed stochastic neighbor embedding(TSNE). The 190 inhibitors was split into a training set and a test set according to the result of a Kohonen’s self-organizing map (SOM) or randomly. Multiple linear regression (MLR) models, support vector machine (SVM) models and two consensus models were built based on the training sets by 20 selected CORINA Symphony descriptors. All the models showed a good prediction of pIC50. The correlation coefficients of all the models were more than 0.7 on the test set. For the training set of consensus Model C1, which performed better than other models, the correlation coefficient(r) achieved 0.909 on the training set, and 0.804 on the test set. The selected molecular descriptors show that hydrogen bond acceptor, atom charges and electronegativities (especially π atom) were important in predicting the activity of HIV-1 integrase LEDGF/p75-IN inhibitors.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Support Vector Machine (SVM) Models for Predicting Inhibitors of the 3′ Processing Step of HIV‐1 Integrase

Cited by 3 publications

References 89 publications

Targeting HIV-1 integrase with strand transfer inhibitors

Targeting HIV-1 integrase with strand transfer inhibitors

Filtered circular fingerprints improve either prediction or runtime performance while retaining interpretability

Quantitative Structure-Activity Relationship Study for HIV-1 LEDGF/p75 Inhibitors

Contact Info

Product

Resources

About