An Inverse QSAR Method Based on Linear Regression and Integer Programming.
QSAR/QSPR
chemoinformatics
integer programming
linear regression
machine learning
materials informatics
molecular design
Journal
Frontiers in bioscience (Landmark edition)
ISSN: 2768-6698
Titre abrégé: Front Biosci (Landmark Ed)
Pays: Singapore
ID NLM: 101612996
Informations de publication
Date de publication:
10 06 2022
10 06 2022
Historique:
received:
16
02
2022
revised:
28
03
2022
accepted:
07
04
2022
entrez:
24
6
2022
pubmed:
25
6
2022
medline:
28
6
2022
Statut:
ppublish
Résumé
Drug design is one of the important applications of biological science. Extensive studies have been done on computer-aided drug design based on inverse quantitative structure activity relationship (inverse QSAR), which is to infer chemical compounds from given chemical activities and constraints. However, exact or optimal solutions are not guaranteed in most of the existing methods. Recently a novel framework based on artificial neural networks (ANNs) and mixed integer linear programming (MILP) has been proposed for designing chemical structures. This framework consists of two phases: an ANN is used to construct a prediction function, and then an MILP formulated on the trained ANN and a graph search algorithm are used to infer desired chemical structures. In this paper, we use linear regression instead of ANNs to construct a prediction function. For this, we derive a novel MILP formulation that simulates the computation process of a prediction function by linear regression. For the first phase, we performed computational experiments using 18 chemical properties, and the proposed method achieved good prediction accuracy for a relatively large number of properties, in comparison with ANNs in our previous work. For the second phase, we performed computational experiments on five chemical properties, and the method could infer chemical structures with around up to 50 non-hydrogen atoms. Combination of linear regression and integer programming is a potentially useful approach to computational molecular design.
Sections du résumé
BACKGROUND
Drug design is one of the important applications of biological science. Extensive studies have been done on computer-aided drug design based on inverse quantitative structure activity relationship (inverse QSAR), which is to infer chemical compounds from given chemical activities and constraints. However, exact or optimal solutions are not guaranteed in most of the existing methods.
METHOD
Recently a novel framework based on artificial neural networks (ANNs) and mixed integer linear programming (MILP) has been proposed for designing chemical structures. This framework consists of two phases: an ANN is used to construct a prediction function, and then an MILP formulated on the trained ANN and a graph search algorithm are used to infer desired chemical structures. In this paper, we use linear regression instead of ANNs to construct a prediction function. For this, we derive a novel MILP formulation that simulates the computation process of a prediction function by linear regression.
RESULTS
For the first phase, we performed computational experiments using 18 chemical properties, and the proposed method achieved good prediction accuracy for a relatively large number of properties, in comparison with ANNs in our previous work. For the second phase, we performed computational experiments on five chemical properties, and the method could infer chemical structures with around up to 50 non-hydrogen atoms.
CONCLUSIONS
Combination of linear regression and integer programming is a potentially useful approach to computational molecular design.
Identifiants
pubmed: 35748264
pii: S2768-6701(22)00551-2
doi: 10.31083/j.fbl2706188
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
188Informations de copyright
© 2022 The Author(s). Published by IMR Press.
Déclaration de conflit d'intérêts
The authors declare no conflict of interest. TA is serving as the guest editor of this journal. We declare that TA had no involvement in the peer review of this article and has no access to information regarding its peer review. Full responsibility for the editorial process for this article was delegated to AK and GP.