A Novel Method for the Inverse QSAR/QSPR based on Artificial Neural Networks and Mixed Integer Linear Programming with Guaranteed Admissibility

Naveed Azam, Rachaya Chiewvanichakorn, Fan Zhang, Aleksandar Shurbevski, Hiroshi Nagamochi, Tatsuya Akutsu

Abstract

Inverse QSAR/QSPR is a well-known approach for computer-aided drug design. In this study, we propose a novel method for inverse QSAR/QSPR using artificial neural network (ANNs) and mixed integer linear programming. In this method, we introduce a feature function f that converts each chemical compound G into a vector f (G) of several descriptors of G. Next, given a set of chemical compounds along with their chemical properties, we construct a prediction function Ψ with an ANN so that Ψ( f (G)) takes a value nearly equal to a given chemical property for many chemical compounds G in the set. Then, given a target value y* of the chemical property, we conversely infer a chemical structure G* having the desired property y* in the following way. We formulate the problem of finding a vector x* such that (i) Ψ(x*) = y* and (ii) there exists a chemical compound G* such that f (G*) = x* (if one exists over all vectors x* in (i)) as a mixed integer linear programming problem (MILP). In an existing method for the inverse QSAR/QSPR, the second condition (ii) was not guaranteed. For acyclic chemical compounds and some chemical properties such as heat of formation, boiling point, and retention time, we conducted computational experiments.

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Paper Citation


in Harvard Style

Azam N., Chiewvanichakorn R., Zhang F., Shurbevski A., Nagamochi H. and Akutsu T. (2020). A Novel Method for the Inverse QSAR/QSPR based on Artificial Neural Networks and Mixed Integer Linear Programming with Guaranteed Admissibility.In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, ISBN 978-989-758-398-8, pages 101-108. DOI: 10.5220/0008876801010108


in Bibtex Style

@conference{bioinformatics20,
author={Naveed Azam and Rachaya Chiewvanichakorn and Fan Zhang and Aleksandar Shurbevski and Hiroshi Nagamochi and Tatsuya Akutsu},
title={A Novel Method for the Inverse QSAR/QSPR based on Artificial Neural Networks and Mixed Integer Linear Programming with Guaranteed Admissibility},
booktitle={Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS,},
year={2020},
pages={101-108},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008876801010108},
isbn={978-989-758-398-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS,
TI - A Novel Method for the Inverse QSAR/QSPR based on Artificial Neural Networks and Mixed Integer Linear Programming with Guaranteed Admissibility
SN - 978-989-758-398-8
AU - Azam N.
AU - Chiewvanichakorn R.
AU - Zhang F.
AU - Shurbevski A.
AU - Nagamochi H.
AU - Akutsu T.
PY - 2020
SP - 101
EP - 108
DO - 10.5220/0008876801010108