EBSCO Logo
Connecting you to content on EBSCOhost
Results
Title

Network-based piecewise linear regression for QSAR modelling.

Authors

Cardoso-Silva, Jonathan; Papageorgiou, Lazaros G.; Tsoka, Sophia

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.

Subjects

REGRESSION analysis; SUPPORT vector machines; LINEAR equations; STRUCTURE-activity relationships; DRUG development

Publication

Journal of Computer-Aided Molecular Design, 2019, Vol 33, Issue 9, p831

ISSN

0920-654X

Publication type

Academic Journal

DOI

10.1007/s10822-019-00228-6

EBSCO Connect | Privacy policy | Terms of use | Copyright | Manage my cookies
Journals | Subjects | Sitemap
© 2025 EBSCO Industries, Inc. All rights reserved