Academic Journal
Network-based piecewise linear regression for QSAR modelling.
| Title: | Network-based piecewise linear regression for QSAR modelling. |
|---|---|
| Authors: | Cardoso-Silva, Jonathan, Papageorgiou, Lazaros G., Tsoka, Sophia |
| Source: | Journal of Computer-Aided Molecular Design; Sep2019, Vol. 33 Issue 9, p831-844, 14p |
| Subject Terms: | Regression analysis, Support vector machines, Linear equations, Structure-activity relationships, Drug development |
| Abstract: | Quantitative Structure-Activity Relationship (QSAR) models are critical in various areas of drug discovery, for example in lead optimisation and virtual screening. Recently, the need for models that are not only predictive but also interpretable has been highlighted. In this paper, a new methodology is proposed to build interpretable QSAR models by combining elements of network analysis and piecewise linear regression. The algorithm presented, modSAR, splits data using a two-step procedure. First, compounds associated with a common target are represented as a network in terms of their structural similarity, revealing modules of similar chemical properties. Second, each module is subdivided into subsets (regions), each of which is modelled by an independent linear equation. Comparative analysis of QSAR models across five data sets of protein inhibitors obtained from ChEMBL is reported and it is shown that modSAR offers similar predictive accuracy to popular algorithms, such as Random Forest and Support Vector Machine. Moreover, we show that models built by modSAR are interpretatable, capable of evaluating the applicability domain of the compounds and serve well tasks such as virtual screening and the development of new drug leads. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Computer-Aided Molecular Design is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 CustomLinks: – Url: https://dx.doi.org/doi:10.1007/s10822-019-00228-6 Name: EDS - Springer Nature Journals (s7799221) Category: fullText Text: View record at Springer |
|---|---|
| Header | DbId: edb DbLabel: Complementary Index An: 139458924 RelevancyScore: 874 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 873.740966796875 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Network-based piecewise linear regression for QSAR modelling. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Cardoso-Silva%2C+Jonathan%22">Cardoso-Silva, Jonathan</searchLink><br /><searchLink fieldCode="AR" term="%22Papageorgiou%2C+Lazaros+G%2E%22">Papageorgiou, Lazaros G.</searchLink><br /><searchLink fieldCode="AR" term="%22Tsoka%2C+Sophia%22">Tsoka, Sophia</searchLink> – Name: TitleSource Label: Source Group: Src Data: Journal of Computer-Aided Molecular Design; Sep2019, Vol. 33 Issue 9, p831-844, 14p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Regression+analysis%22">Regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Linear+equations%22">Linear equations</searchLink><br /><searchLink fieldCode="DE" term="%22Structure-activity+relationships%22">Structure-activity relationships</searchLink><br /><searchLink fieldCode="DE" term="%22Drug+development%22">Drug development</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Quantitative Structure-Activity Relationship (QSAR) models are critical in various areas of drug discovery, for example in lead optimisation and virtual screening. Recently, the need for models that are not only predictive but also interpretable has been highlighted. In this paper, a new methodology is proposed to build interpretable QSAR models by combining elements of network analysis and piecewise linear regression. The algorithm presented, modSAR, splits data using a two-step procedure. First, compounds associated with a common target are represented as a network in terms of their structural similarity, revealing modules of similar chemical properties. Second, each module is subdivided into subsets (regions), each of which is modelled by an independent linear equation. Comparative analysis of QSAR models across five data sets of protein inhibitors obtained from ChEMBL is reported and it is shown that modSAR offers similar predictive accuracy to popular algorithms, such as Random Forest and Support Vector Machine. Moreover, we show that models built by modSAR are interpretatable, capable of evaluating the applicability domain of the compounds and serve well tasks such as virtual screening and the development of new drug leads. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Journal of Computer-Aided Molecular Design is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edb&AN=139458924 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10822-019-00228-6 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 831 Subjects: – SubjectFull: Regression analysis Type: general – SubjectFull: Support vector machines Type: general – SubjectFull: Linear equations Type: general – SubjectFull: Structure-activity relationships Type: general – SubjectFull: Drug development Type: general Titles: – TitleFull: Network-based piecewise linear regression for QSAR modelling. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Cardoso-Silva, Jonathan – PersonEntity: Name: NameFull: Papageorgiou, Lazaros G. – PersonEntity: Name: NameFull: Tsoka, Sophia IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2019 Type: published Y: 2019 Identifiers: – Type: issn-print Value: 0920654X Numbering: – Type: volume Value: 33 – Type: issue Value: 9 Titles: – TitleFull: Journal of Computer-Aided Molecular Design Type: main |
| ResultId | 1 |