Cardoso-Silva, J;
Papageorgiou, LG;
Tsoka, S;
(2019)
Network-based piecewise linear regression for QSAR modelling.
Journal of Computer-Aided Molecular Design
, 33
(9)
pp. 831-844.
10.1007/s10822-019-00228-6.
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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.
| Type: | Article |
|---|---|
| Title: | Network-based piecewise linear regression for QSAR modelling |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/s10822-019-00228-6 |
| Publisher version: | https://doi.org/10.1007/s10822-019-00228-6 |
| Language: | English |
| Additional information: | © The Author(s) 2019. Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
| Keywords: | QSAR regression, Piecewise linear regression, Mathematical programming, Mixed integer programming |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Chemical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10088424 |
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